Personalized Diabetes Care Research Articles

Therapeutic autonomy for residents living with diabetes in institutions

The autonomy of elderly people living with diabetes must be encouraged, even when they are institutionalized. A comprehensive assessment is the first step towards maintaining this therapeutic autonomy. Tools and procedures are available to promote personalized diabetes care plans.

DEMIGOD: A Low-Cost Microcontroller-Based Closed-Loop System Integrating Nanoengineered Sweat-Based Glucose Monitoring and Controlled Transdermal Nanoemulsion Release of Hypoglycemic Treatment with a Software Application for Noninvasive Personalized Diabetes Care.

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system.

Transforming diabetes care with continuous glucose monitoring: Rationale for expanded eligibility criteria.

Randomized trials and large retrospective database studies have shown that individuals treated with noninsulin therapies experience the same glycemic benefits from continuous glucose monitoring (CGM) use as those treated with more intensive treatment regimens. However, many policy makers and payers are reluctant to provide CGM coverage for these patients. Although the recent American Diabetes Association guidelines have taken an important first step in recommending that CGM should be offered to all adults treated with basal insulin who are capable of using it, clinicians should consider the acute and long-term consequences of persistent hyperglycemia in all of their patients with diabetes. This article describes how the use of the FreeStyle Libre 2 CGM System (Abbott Diabetes Care, Alameda, CA) improved glycemic outcomes and facilitates personalized diabetes care in two type 2 diabetes patients treated with noninsulin therapies.

Exploration of Machine Learning Model for Diabetes Prediction

Diabetes is a metabolic disorder characterized by a malfunction in insulin release, resulting in a rise in blood sugar levels in the body. Diabetes diagnosis must be made on time and precisely in order to be effective and enhance patient outcomes. The diabetes management presents a formidable challenge in modern healthcare, demanding a combination of timely interventions, precise data analysis, and personalized medical services. Furthermore, there exists a growing demand for advanced predictive models that not only provide accurate forecasts but also offer transparency and interpretability. The study objectives are to develop an innovative machine learning model for data-driven diabetes prediction and medical services. To identify the machine learning model that best suits the proposed system, a literature review related to different methods used in diagnosing diabetes is conducted. The merits and demerits of each existing system were identified to devise a proposed model that seamlessly integrates data from various sources, including blood glucose levels and patient health information. Based on the review, the study suggests an innovative machine learning model that utilizes the Explainable Decision Tree Model, leveraging cloud computing to analyse diabetes patient data and make predictions. The integration of cloud computing allows for seamless data integration from various sources. This research project represents a significant step forward in personalized diabetes care, enabling patients to proactively manage their condition while providing healthcare professionals with a powerful tool for delivering tailored medical services.

Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data.

The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications. The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge-based temporal abstraction with numerical vectorization. The k-means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM. By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic β-cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set. Subtyping patients with T2DM using CGM data may help identify high-risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.

Impact of personalized diabetes care on distress and treatment satisfaction in people with breast cancer.

In patients with breast cancer (BCa) and diabetes (DM), diabetes distress (DD) and treatment satisfaction (DTS) can influence BCa management and outcomes. We assessed the impact of implementing a personalized diabetes care model in patients with BCa. Patients in active treatment or surveillance for BCa with an HbA1c > 53 mmol/mol (7%) or random blood glucose >11.1 mmol/L were included. Participants were offered continuous glucose monitoring (CGM), virtual care and a dedicated diabetes provider for 6 months. Primary outcomes included DD measured by the Diabetes Distress Survey (DDS) and DTS measured by the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Questionnaires were conducted at 0, 3 and 6 months. Thirty-one women were enrolled (median age 61, IQR 49.0-69.0). Compared to baseline, the mean DDS score was lower at both 3 months (2.2 vs. 1.8 [n = 27], p = 0.004, SD = 0.70) and 6 months (2.3 vs. 1.8 [n = 23], p = 0.002, SD = 0.70). The mean DTSQ score was higher at 3 months (baseline: 20.5 vs. 3 months: 28.7 [n = 28], p < 0.001, SD = 9.2) and 6 months (baseline: 20.4 vs. 6 months: 30.0 [n = 26], p < 0.001, SD = 9.7). Personalized diabetes care models that emphasize remote management and optimize access for those with BCa may lower DD and improve DTS.

Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes.

Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities. We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels. The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning. As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Standardising personalised diabetes care across European health settings: A person-centred outcome set agreed in a multinational Delphi study.

Standardised person-reported outcomes (PRO) data can contextualise clinical outcomes enabling precision diabetes monitoring and care. Comprehensive outcome sets can guide this process, but their implementation in routine diabetes care has remained challenging and unsuccessful at international level. We aimed to address this by developing a person-centred outcome set for Type 1 and Type 2 diabetes, using a methodology with prospects for increased implementability and sustainability in international health settings. We used a three-round questionnaire-based Delphi study to reach consensus on the outcome set. We invited key stakeholders from 19 countries via purposive snowball sampling, namely people with diabetes (N = 94), healthcare professionals (N = 65), industry (N = 22) and health authorities (N = 3), to vote on the relevance and measurement frequency of 64 previously identified clinical and person-reported outcomes. Subsequent consensus meetings concluded the study. The list of preliminary outcomes was shortlisted via the consensus process to 46 outcomes (27 clinical outcomes and 19 PROs). Two main collection times were recommended: (1) linked to a medical visit (e.g. diabetes-specific well-being, symptoms and psychological health) and (2) annually (e.g. clinical data, general well-being and diabetes self management-related outcomes). PROs are often considered in a non-standardised way in routine diabetes care. We propose a person-centred outcome set for diabetes, specifically considering psychosocial and behavioural aspects, which was agreed by four international key stakeholder groups. It guides standardised collection of meaningful outcomes at scale, supporting individual and population level healthcare decision making. It will be implemented and tested in Europe as part of the H2O project.

Inter-relationships between gastric emptying and glycaemia: Implications for clinical practice.

Gastric emptying (GE) exhibits a wide inter-individual variation and is a major determinant of postprandial glycaemia in health and diabetes; the rise in blood glucose following oral carbohydrate is greater when GE is relatively more rapid and more sustained when glucose tolerance is impaired. Conversely, GE is influenced by the acute glycaemic environment acute hyperglycaemia slows, while acute hypoglycaemia accelerates it. Delayed GE (gastroparesis) occurs frequently in diabetes and critical illness. In diabetes, this poses challenges for management, particularly in hospitalised individuals and/or those using insulin. In critical illness it compromises the delivery of nutrition and increases the risk of regurgitation and aspiration with consequent lung dysfunction and ventilator dependence. Substantial advances in knowledge relating to GE, which is now recognised as a major determinant of the magnitude of the rise in blood glucose after a meal in both health and diabetes and, the impact of acute glycaemic environment on the rate of GE have been made and the use of gut-based therapies such as glucagon-like peptide-1 receptor agonists, which may profoundly impact GE, in the management of type 2 diabetes, has become commonplace. This necessitates an increased understanding of the complex inter-relationships of GE with glycaemia, its implications in hospitalised patients and the relevance of dysglycaemia and its management, particularly in critical illness. Current approaches to management of gastroparesis to achieve more personalised diabetes care, relevant to clinical practice, is detailed. Further studies focusing on the interactions of medications affecting GE and the glycaemic environment in hospitalised patients, are required.

Standardising personalised diabetes care across European health settings: a person-centred outcome set agreed in a multinational Delphi study

Standardising personalised diabetes care across European health settings: a person-centred outcome set agreed in a multinational Delphi study

940-P: Personalized Diabetes Care Reduces Distress and Improves Treatment Satisfaction in Patients with Breast Cancer

In patients with breast cancer (BCa) and diabetes (DM) , diabetes distress (DD) and treatment satisfaction (DTS) can influence cancer management and outcomes, yet there is limited data on how important these factors are in the BCa DM population. In this pilot study we assessed the impact of implementing a personalized DM care model on DD and DTS in patients with BCa. Patients in active treatment or surveillance for BCa who had uncontrolled DM (A1c &amp;gt; 7% or random blood glucose &amp;gt; 200 mg/dL) were included. All participants were offered continuous glucose monitoring (CGM) , virtual care and a dedicated diabetes provider with monthly visits for six months. The primary outcomes were change in DD measured by the Diabetes Distress Survey (DDS) and DTS measured by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) . Questionnaires were conducted at 0, 3 and 6 months and changes were assessed using paired T test. Thirty-one women were enrolled (median age 61) . When each participant was assessed for changes in DD, a significant decrease in mean DD was observed at both 3 (baseline: 2.2 vs. 3 months: 1.8, p=0.004) and 6 months (baseline: 2.4 vs. 6 months: 1.9, p= 0.010) . Participants with high DD, DDS score ≥ 3, N=6 (19%) , had a significant decrease in DD at both 3 (baseline: 3.8 vs. 3 months: 2.7, p=0.016) and 6 months (baseline: 3.8 vs. 6 months: 2.7, p=0.018) . Those with low to moderate DD (DDS &amp;lt;3) showed no difference at either 3 (baseline: 1.7 vs. 3 months: 1.5, p=0.082) or 6 months (baseline: 1.8 vs. 6 months: 1.5, p=0.196) . Additionally, a significant increase in the mean DTSQ score was observed at both 3 (baseline: 20.3 vs. 3 months: 28.7, p&amp;lt;0.001) and 6 months (baseline: 19.9 vs. 6 months: 29.3, p=0.002) . Conclusions: Among women with BCa, diabetes distress is prevalent. Personalized DM care models that emphasize remote management and optimize access are therefore not only acceptable to BCa patients, but also lower DM distress and improve treatment satisfaction. Disclosure J.Harrod: None. Y.M.Cheung: None. K.M.Fowler: n/a. M.E.Hughes: None. L.Min: Research Support; Bristol-Myers Squibb Company, Lilly. S.Tolaney: Consultant; 4D Pharma PLC, AstraZeneca, Athenex, BeyondSpring Pharmaceuticals, Bristol-Myers Squibb Company, Certara, Inc., Chugai Pharmaceutical Co., Ltd., CytomX, Daiichi Sankyo, Eisai Co., Ltd., Eli Lilly and Company, Ellipses Pharmaceuticals, Genentech, Inc., Gilead Sciences, Inc., Kyowa Kirin Co., Ltd., Merck &amp; Co., Inc., Mersana Therapeutics, Novartis AG, Odonate, OncoPep, OncoSec Medical Inc, OncXerna, Pfizer Inc., Puma, Sanofi, Seattle Genetics, Inc., Zentalis, Zymeworks, Research Support; AstraZeneca, Bristol-Myers Squibb Company, Cyclacel, Eisai Co., Ltd., Eli Lilly and Company, Exelixis, Genentech, Inc., Gilead Sciences, Inc., Merck &amp; Co., Inc., Nanostring, Nektar, Novartis AG, Odonate, Pfizer Inc., Sanofi, Seattle Genetics, Inc. M.E.Mcdonnell: Advisory Panel; Everlywell, Inc., Research Support; Lilly, Stock/Shareholder; Abbott Diabetes. Funding Lilly USA, LLC

Less Timely Initiation of Glucose-Lowering Medication Among Younger and Male Patients With Diabetes and Similar Initiation of Blood Pressure-Lowering Medication Across Age and Sex: Trends Between 2015 and 2020.

Aims: We aimed to assess trends in glycosylated hemoglobin A1c (HbA1c) and systolic blood pressure (SBP) thresholds at initiation of glucose- and blood pressure-lowering medication among patients with type 2 diabetes and assess the influence of age and sex on these trends. Materials and Methods: We used the Groningen Initiative to ANalyze Type 2 diabetes Treatment (GIANTT) primary care database. Patients initiating a first non-insulin glucose-lowering or any blood pressure-lowering medication between 2015 and 2020 with an HbA1c or SBP measurement in the 120 days before initiation were included. We used multilevel regression analyses adjusted for potential confounders to assess the influence of calendar year, age or sex, and the interaction between calendar year and age or sex on trends in HbA1c and SBP thresholds at initiation of medication. Results: We included 2,671 and 2,128 patients in the analyses of HbA1c and SBP thresholds, respectively. The overall mean HbA1c threshold at initiation of glucose-lowering medication significantly increased from 7.4% in 2015 to 8.0% in 2020 (p < 0.001), and particularly in the younger age groups. Compared to patients ≥80 years, patients aged 60–69 years initiated medication at lower levels mainly in the early years. Patients <60 years and between 70–79 years initiated medication at similar levels as patients ≥80 years. Females initiated medication at lower levels than males throughout the study period (p < 0.001). The mean SBP threshold at initiation of blood pressure-lowering medication varied from 145 to 149 mmHg without a clear trend (p = 0.676). There were no differences in SBP thresholds between patients of different ages or sex. Conclusion: The rising trend in the HbA1c threshold for initiating glucose-lowering medication in the lower age groups was unexpected and requires further investigation. Males appear to receive less timely initiation of glucose-lowering medication than females. The lack of higher thresholds for the oldest age group or lower thresholds for the youngest age group in recent years is not in line with the age-related recommendations for personalized diabetes care and calls for health systems interventions.

Challenges Involved in Incorporating Personalised Treatment Plan as Routine Care of Patients with Diabetes

Diabetes is a heterogenous disorder, and patients with this disorder vary considerably in their clinical presentation, response to therapy and risk of complications. Expanding knowledge about the pathophysiology of various forms of diabetes has raised the possibility that diagnostic and therapeutic modalities can be tailored to the individual patient in a personalized manner. The recent publication of a Consensus Statement on precision diabetes care underlines the major strides made in this field in the recent past. However, while personalized diabetes care has the potential to significantly improve outcomes in patients with diabetes in a safe and cost-effective manner, its wider application presents several challenges, especially in resource-strained settings. These challenges pertain equally to precision diagnostics, precision therapeutics and precision monitoring. This article discusses some of the important challenges that care providers are likely to face in applying the personalized approach in caring for their patients with diabetes, in the context of diagnosis and management of type 1 diabetes, type 2 diabetes and monogenic forms of diabetes. Suggestions are also presented for overcoming some of these challenges.

Changes in blood pressure thresholds for initiating antihypertensive medication in patients with diabetes: a repeated cross-sectional study focusing on the impact of age and frailty

ObjectiveTo assess trends in systolic blood pressure (SBP) thresholds at initiation of antihypertensive treatment in patients with type 2 diabetes and the impact of age and frailty on these trends.Study...

Aging and Diabetes: Shifting the Risk Trajectory With High-Sensitivity Cardiac Troponins.

Diabetes is a serious health challenge for the aging population, contributing to morbidity and mortality primarily through increased risk for ischemic heart disease, heart failure, and stroke (1). Approximately 25–30% of adults over the age of 65 years have diabetes (2,3), and this proportion is expected to increase rapidly in the coming decades (4). Older individuals with diabetes also have higher rates of comorbidities including functional disability, accelerated muscle loss, chronic kidney disease, and coexisting cardiovascular risk factors compared with those without diabetes (3). Since multiple comorbidity among older adults with diabetes is common, individualized risk assessment to guide therapy using standard risk stratification tools has proven to be challenging. Therefore, new strategies are needed to improve risk assessment and inform use of novel and emerging therapies for patients with diabetes given important risk/benefit trade-offs between efficacy and adverse effects in the older adult population. Incorporation of high-sensitivity cardiac troponin (hs-cTn) into risk stratification and treatment decision algorithms is one such strategy that has the potential to improve personalized diabetes care. hs-cTn–based risk stratification (using both the hs-cTnT and hs-cTnI isoforms) has been extensively investigated in multiple population settings and for multiple outcomes including coronary artery disease, heart failure, stroke, and mortality (5–8). The association is strongest for heart failure and mortality and weakest for ischemic events (6). When measured in ambulatory individuals without cardiac symptoms, hs-cTn is a measure of subclinical myocardial injury and provides independent, additional prognostic information beyond other cardiac biomarkers and diagnostic tests such as electrocardiographic left ventricular hypertrophy, N-terminal pro B-type natriuretic peptide, and coronary artery calcium (9,10). Importantly, although hs-cTn is commonly elevated beyond the detectable threshold in older adults (∼2/3 will have a level …

Correlation between self-monitored mean blood glucose and average plasma glucose estimated from glycated haemoglobin in patients attending the diabetes clinic at Dr George Mukhari Academic Hospital, Pretoria, South Africa.

Glycated haemoglobin (HbA1c) has been used for decades as a measure of chronic glycaemia. A simple linear relationship between HbA1c values and mean blood glucose (MBG) has been identified and led to conversion of HbA1c values into estimated average glucose (eAG) levels, following the findings of the A1c-Derived Average Glucose (ADAG) Study Group. The intention was to help patients with diabetes mellitus (DM) understand their glycaemic control better, as eAG is reported in the same units as self-monitored glucose levels. However, factors other than glycaemia have been found to affect the relationship between HbA1c and MBG. To: (i) determine the relationship between self-monitored MBG levels and HbA1c values; and (ii) evaluate the correlation between MBG levels and eAG levels calculated from HbA1c values using the regression equation derived from the ADAG Study Group in black South African patients with DM. This was a prospective observational study of 96 diabetic patients. MBG levels were calculated using glucose measurements downloaded from the glucose meters for the previous 90 days (3 months). High-performance liquid chromatography was used for measurement of HbA1c values, collected at the end of 3 months. eAG was calculated using the regression equation from the ADAG Study Group, as follows: eAG (mmol/L) = 1.5944 × HbA1c (NGSP, %) - 2.594. A positive correlation was found between MBG and HbA1c in all participants (R2=0.69, p&lt;0.0001). There was a wide range of MBG levels for any given HbA1c value. Clinically significant differences between MBG and eAG were found, with a ≥10% difference in 65.6% of the participants. eAG overestimated MBG in ~71.8% of the study population, with an overestimation of ≥1.6 mmol/L (28.7 mg/dL, equivalent to a 1% change in HbA1c value) in ~50% of the total study population. Our findings showed an imperfect relationship between MBG levels and HbA1c values. eAG significantly overestimated MBG, and this disagreement may cause confusion among both patients and clinicians. The risk of hypoglycaemic episodes may also increase if HbA1c and eAG alone are used to intensify therapy. We recommend that the use of eAG should be validated prior to implementation in clinical practice. It would be ideal to evaluate the relationship between average glucose and HbA1c in each individual patient in order to provide more personalised diabetes care.

Innovations in personalised diabetes care and risk management.

Type 2 diabetes is associated with an increased risk of developing macro and microvascular complications. Nevertheless, there is substantial heterogeneity between people with type 2 diabetes in their risk of developing such complications. Personalised medicine for people with type 2 diabetes may aid in efficient and tailored diabetes care for those at increased risk of developing such complications. Recently, progress has been made in the development of personalised diabetes care in several areas. Particularly for the risk prediction of cardiovascular disease, retinopathy and nephropathy, innovative methods have been developed for prediction and tailored monitoring or treatment to prevent such complications. For other complications or subpopulations of people with type 2 diabetes, such as the frail elderly, efforts are currently ongoing to develop such methods. In this review, we discuss the recent developments in innovations of personalised diabetes care for different complications and subpopulations of people with type 2 diabetes, their performance and modes of application in clinical practice.

The impact of cancer on diabetes outcomes

BackgroundSurvival from many cancer types is steadily increasing, and as a result, a growing number of cancer patients will live with other chronic diseases, of which diabetes is one of the most prevalent. This study aims to describe the impact of cancer on health outcomes in patients with type 2 diabetes and to compare the effectiveness of a multifactorial intervention in diabetes patients with and without cancer.MethodsThe randomized controlled trial Diabetes Care in General Practice (DCGP) included 1381 patients newly diagnosed with type 2 diabetes. Patients were randomized to either six years of structured personal diabetes care or routine care. In a post hoc analysis, we followed patients for 19 years in Danish national registries for the occurrence of diabetes-related outcomes. We used Cox regression models to estimate hazard ratios for outcomes.ResultsAt diagnosis 48 patients had cancer, and 243 patients were diagnosed with cancer during follow up. Patients with diabetes and cancer had excess all-cause mortality (HR 3.33; 95%CI 2.72–4.06), as well as an increased incidence of myocardial infarction (HR 1.76; 95%CI 1.29–2.39) and any diabetes-related outcome (HR 1.36; 95%CI 1.07–1.71). The intervention reduced the risk of both these endpoints in patients without cancer. Furthermore, there was no statistically significant difference in the effectiveness of the intervention among patients with and without cancer.ConclusionsDiabetes patients with cancer had an increased risk of myocardial infarction and any diabetes-related outcome. The observed positive effect of structured personal diabetes care on clinical outcomes did not differ between patients with and without cancer. Attention to and prevention of diabetes complications in patients with both type 2 diabetes and cancer is warranted.Trial registrationClinicalTrials.gov NCT01074762 (February 24, 2010).

Technological advances shaping diabetes care.

To emphasize the current unmet needs for patients with diabetes and evaluate the recent technological advances in the diabetes field and summarize upcoming technologies in diabetes care. This review highlights emerging diabetes technologies and patient-centered diabetes management. A review of the literature showed that there is a clear benefit of using diabetes technologies in diabetes care. Recently, the US Food and Drug Administration (FDA) created a new category of Class II integrated continuous glucose monitoring (iCGM) devices and announced new guidelines to accelerate the approval of future products. With the first-generation hybrid-closed loop, a new era opened in automated insulin delivery systems. Diabetes coaching, apps, and remote monitoring technologies eased access to the providers and increased patient's self-confidence for diabetes management. Improvements in diabetes technologies will hopefully overcome unmet needs for patients with diabetes and improve health outcomes. Patients will benefit from the upcoming technologies in their day-to-day diabetes management while providers may monitor patients remotely with ease and efficiently. These developments will decrease diabetes burden, improve quality of life, and open a new era of personalized diabetes care.

Concanavalin A-sugar affinity based system: Binding interactions, principle of glucose-responsiveness, and modulated insulin release for diabetes care

The glucose-responsive behavior of the Con A-sugar affinity based hydrogels, microgels, films and nanoparticles has been widely investigated in the application of drug delivery and biosensors. However, the principle or mechanism of such systems is not well known in literature. In this study, for the first time, the glucose-responsive principle (or principle) of the Con A-sugar based system was detailedly explained through the binding interactions test by isothermal titration calorimetry and ligand binding theory. The study also successfully resolved the controversy regarding the principle of such systems in literature. The other contribution of the study is the experimental validation that the insulin-loaded microgels with different hydrogel-network compositions (based on Con A-sugar affinity) respond to different glucose concentrations. Particularly, the result of the in vitro insulin release suggests that the microgels be able to maintain bolus and basal insulin release in response to different glucose concentrations and the network composition be able to affect the burst release, release rate and overall release amount of insulin. Further, the released insulin has been shown to remain active and the microgels possess no in vitro cytotoxicity to HDF cells. The second contribution is a first step towards the realization of personalized diabetes care.