Insulin and Its Cardiovascular Effects: What Is the Current Evidence?

PURPOSE OF REVIEW: In this article, we examine the nature of the complex relationship between insulin and cardiovascular disease. With metabolic abnormalities comes increased risk for cardiovascular complications. We discuss the key factors implicated in development and progression of cardiovascular disease, its relationship to insulin therapy, and what can be learned from large, recent cardiovascular outcome studies. RECENT FINDINGS: Preclinical studies suggest that insulin has positive effects of facilitating glucose entry into cells and maintaining euglycemia and negative effects of favoring obesity and atherogenesis under certain conditions. Confounding this relationship is that cardiovascular morbidity is linked closely to duration and control of diabetes, and insulin is often used in patients with diabetes of longer duration. However, more recent clinical studies examining the cardiovascular safety of insulin therapy have been reassuring. Diabetes and cardiovascular outcomes are closely linked. Many studies have implicated insulin resistance and hyperinsulinemia as a major factor for poor cardiovascular outcomes. Additional studies link the anabolic effects of therapeutic insulin to weight gain, along with hypoglycemia, which may further aggravate cardiovascular risk in this population. Though good glycemic control has been shown to improve microvascular risks in type 1 and type 2 diabetes, what are the known cardiovascular effects of insulin therapy? The ORIGIN trial suggests at least a neutral effect of the basal insulin glargine on cardiovascular outcomes. Recent studies have demonstrated that ultra-long-acting insulin analogs like insulin degludec are non-inferior to insulin glargine with regard to cardiovascular outcomes.

The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.

INTRODUCTION: Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired. METHODS: We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event. RESULTS: The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians. CONCLUSIONS: Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

The critical elements of digital health in diabetes and cardiometabolic care.

Digital innovations provide novel opportunities to individualize a person's care to best match their lifestyle needs and circumstances and to support them as they live their daily lives with diabetes. These innovations also serve to provide actionable data and insights for the care team giving them a "Webb telescope-like" view into their individual self-management journey, allowing them to see what cannot be seen during infrequent and limited office visits, thereby facilitating collaboration and communication to optimize the care plan on a timely basis. Technology advances are enabling diabetes care to transition from episodic, synchronous, primarily in-person care to include synchronous virtual care options and to continuous, on-demand, data-informed, asynchronous digital care better matching the demands of living with a relentless 24/7 chronic condition. In this paper we will discuss the critical elements and considerations in designing and implementing successful diabetes digital health tools in clinical practice.

Impact of a Combined Continuous Glucose Monitoring-Digital Health Solution on Glucose Metrics and Self-Management Behavior for Adults With Type 2 Diabetes: Real-World, Observational Study.

BACKGROUND: The BlueStar (Welldoc) digital health solution for people with diabetes incorporates data from multiple devices and generates coaching messages using artificial intelligence. The BlueStar app syncs glucose data from the G6 (Dexcom) real-time continuous glucose monitoring (RT-CGM) system, which provides a glucose measurement every 5 minutes. OBJECTIVE: The objective of this real-world study of people with type 2 diabetes (T2D) using the digital health solution and RT-CGM was to evaluate change in glycemic control and engagement with the program over 3 months. METHODS: Participants were current or former enrollees in an employer-sponsored health plan, were aged 18 years or older, had a T2D diagnosis, and were not using prandial insulin. Outcomes included CGM-based glycemic metrics and engagement with the BlueStar app, including logging medications taken, exercise, food details, blood pressure, weight, and hours of sleep. RESULTS: Participants in the program that met our analysis criteria (n=52) were aged a mean of 53 (SD 9) years; 37% (19/52) were female and approximately 50% (25/52) were taking diabetes medications. The RT-CGM system was worn 90% (SD 8%) of the time over 3 months. Among individuals with suboptimal glycemic control at baseline, defined as mean glucose >180 mg/dL, clinically meaningful improvements in glycemic control were observed, including reductions in a glucose management indicator (-0.8 percentage points), time above range 181-250 mg/dL (-4.4 percentage points) and time above range >250 mg/dL (-14 percentage points; all P<.05). Time in range 70-180 mg/dL also increased by 15 percentage points (P=.016) in this population, which corresponds to an increase of approximately 3.5 hours per day in the target range. Over the 3-month study, 29% (15/52) of participants completed at least one engagement activity per week. Medication logging was completed most often by participants (23/52, 44%) at a rate of 12.1 (SD 0.8) events/week, and this was closely followed by exercise and food logging. CONCLUSIONS: The combination of an artificial intelligence-powered digital health solution and RT-CGM helped people with T2D improve their glycemic outcomes and diabetes self-management behaviors.

Engagement and Outcomes Associated with Contextual Annotation Features of a Digital Health Solution.

BACKGROUND: Digital health solutions targeting diabetes self-care are popular and promising, but important questions remain about how these tools can most effectively help patients. Consistent with evidence of the salutary effects of note-taking in education, features that enable annotation of structured data entry might enhance the meaningfulness of the interaction, thereby promoting persistent use and benefits of a digital health solution. METHOD: To examine the potential benefits of note-taking, we explored how patients with type 2 diabetes used annotation features of a digital health solution and assessed the relationship between annotation and persistence in engagement as well as improvements in glycated hemoglobin (A1C). Secondary data from 3142 users of the BlueStar digital health solution collected between December 2013 and June 2017 were analyzed, with a subgroup of 372 reporting A1C lab values. RESULTS: About a third of patients recorded annotations while using the platform. Annotation themes largely reflected self-management behaviors (diet, physical activity, medication adherence) and well-being (mood, health status). Early use of contextual annotations was associated with greater engagement over time and with greater improvements in A1C. CONCLUSIONS: Our research provides preliminary evidence of the benefits of annotation features in a digital health solution. Future research is needed to assess the causal impact of note-taking and the moderating role of thematic content reflected in notes.

Hypoglycemia prediction using machine learning models for patients with type 2 diabetes.

Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. We validated our model using multiple data sets. In addition, we trained a second model, which used patient SMBG values and information about patient medication administration. The optimal number of SMBG values needed by the model was approximately 10 per week. The sensitivity of the model for predicting a hypoglycemia event in the next 24 hours was 92% and the specificity was 70%. In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%. Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models-which have been validated retrospectively and if implemented in real time-could be useful tools for reducing hypoglycemia in vulnerable patients.

Diabetes treatment in 2025: can scientific advances keep pace with prevalence?

Despite the known benefits of a healthy lifestyle, many individuals find it hard to maintain such a lifestyle in our modern world, which facilitates sedentary behavior and overeating. As a consequence, the prevalence of type 2 diabetes mellitus is predicted to increase dramatically over the coming years. Will developments in treatments be able to counteract the resulting impact on morbidity and mortality? The various lines of research can be grouped into three main categories: technological, biological, and pharmacological. Technological solutions are focused on the delivery of insulin and glucagon via an artificial pancreas, and components of the system are already in use, suggesting this option may well be available within the next 10 years. Of the biological solutions, pancreas transplants seem unlikely to be used widely, and islet cell transplants have also been hampered by a lack of appropriate donor tissue and graft survival after transplant. However, significant progress has been made in these areas, and additional research suggests manipulating other cell types to replace beta cells may be a viable option in the longer term. The last category, pharmacological research, appears the most promising for significantly reducing the burden of type 2 diabetes mellitus. In recent years, research has concentrated on reducing blood glucose, and the increasing pace of research has been reflected in a growing number of antidiabetic agents. In the past few years, studies of the complementary approach of protecting cells from the damaging effects of high blood glucose have also been reported, as has research into the control of energy intake and energy expenditure. Evidence from studies of dietary restriction and bariatric surgery suggests it may be possible to reset metabolism to effectively cure diabetes, and research into pharmacological agents that could selectively restore energy balance is currently the most exciting prospect for future treatments for people with type 2 diabetes mellitus.

Incidence of extreme hyperglycemia and hypoglycemia in hospitalized patients during the month of July in teaching hospitals.

INTRODUCTION: Blood glucose control has been found to be an important component in the care of hospitalized patients. Maintaining blood glucose within a target range using insulin intensively is a challenging task for physicians and requires skill and experience. We hypothesized that there may be more hyper- and hypoglycemia in July in teaching hospitals when new resident physicians begin their training. METHODS: We reviewed point-of-care blood glucose data from hospitalized patients at four community teaching hospitals for 2010. We defined severe hypoglycemia as blood glucose < 41 mg/dL and severe hyperglycemia as blood glucose > 399 mg/dL. Occurrence of hyper- and hypoglycemic events was assessed overall at the particular hospital globally and based on individual nursing units. Monthly occurrence rates were compared against the annual mean for that unit. RESULTS: The occurrence of hyper- and hypoglycemic events in July 2010 did not differ from the mean annual percentage of events at the applicable hospital. However, when the data were analyzed by the nursing unit, these extreme glucose events were significantly more common in 4 of the 11 units studied. Three of those four units were resident teaching units. CONCLUSIONS: These data suggest that there is some potential for increased risk of extreme hyper- and hypoglycemia at teaching hospitals in July, when new residents begin training.

Hypoglycemia in the hospital.

Hypoglycemia is a common adverse event affecting hospitalized patients with diabetes. This paper reviews the data regarding optimization of glucose in hospitalized patients, discusses the scope and significance of hypoglycemia in the hospital, and makes recommendations on how to reduce the risk of this serious adverse event.

Add-on therapies to metformin for type 2 diabetes.

IMPORTANCE OF THE FIELD: As of 2010, approximately 285 million people worldwide have diabetes; that number is estimated to increase to 439 million by 2030. The majority of these individuals (> 90%) have type 2 diabetes, a chronic and progressive disease. AREAS COVERED IN THIS REVIEW: Metformin monotherapy is a safe and effective option. However, its effects on glycemia are typically of limited durability. Progressive loss of ß-cell function and failure of metformin monotherapy to control glucose adequately prompt the addition of other oral antidiabetic drugs, such as sulfonylureas and thiazolidinediones, which have their own limitations. Evidence suggests that incretin-based agents can successfully achieve glycemic control while potentially providing cardiovascular and ß-cell-function benefits. WHAT THE READER WILL GAIN: Knowledge of the available clinical evidence on the incretin-based therapies and other pharmacotherapeutic options for patients with type 2 diabetes who fail first-line therapy with metformin, through an analysis of improved glycemic parameters and overall risk:benefit profiles. TAKE HOME MESSAGE: Traditional oral antidiabetic agents, recommended as first- and second-line therapies in patients with type 2 diabetes inadequately controlled with diet/exercise or monotherapy, have limited durability of effect and are associated with an increased risk of adverse events. Glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors provide glycemic control and are promising additions to the pharmacotherapeutic armamentarium.

Conversion from intravenous insulin to subcutaneous insulin after cardiovascular surgery: transition to target study.

BACKGROUND: No study of transition from intravenous to subcutaneous insulin after cardiac surgery with dose based on percentage of intravenous total daily insulin (TDI) has reported a clearly superior regimen for achieving target blood glucose. We compared three first-dose transition strategies for insulin glargine: two based on TDI alone and one that also took body weight into account. METHODS: Mostly obese, type 1 and type 2 diabetes patients (n = 223) undergoing cardiac surgery were randomized to receive insulin glargine subcutaneously at 60% or 80% of TDI or in a dose based on TDI and body weight. RESULTS: Transition to subcutaneous insulin occurred 27.4 ± 6.6 h after surgery. Over the study period, mean proportion of blood glucose values within target range (80-140 mg/dL) were 0.34 ± 0.24, 0.35 ± 0.24, and 0.36 ± 0.22 in the 60% TDI, 80% TDI, and weight-based groups, respectively. This difference was not significant. Significantly more insulin corrections were needed in the 60% TDI group than in the weight-based group. There was only one incidence of hypoglycemia (blood glucose < 40 mg/dL). CONCLUSIONS: No subcutaneous insulin regimen implemented approximately 1 day after cardiac surgery showed significantly better control of blood glucose over the 3-day study period. Further studies are needed to determine optimal formulae for effecting an early transition to subcutaneous insulin after cardiac surgery or whether it is preferable and/or necessary to continue intravenous insulin therapy for an additional period of time.

Distinguishing among incretin-based therapies. Glucose-lowering effects of incretin-based therapies.

Extensive experience from randomized clinical trials demonstrates the efficacy of GLP-1 agonists and DPP-4 inhibitors as monotherapy and in combination with metformin and other agents, although reductions in FPG and PPG, and consequently A1C, are greater with GLP-1 agonists than with DPP-4 inhibitors. This difference may result from the pharmacologic levels of GLP-1 activity that are achieved with the GLP-1 agonists and their direct action on the GLP-1 receptor. The GLP-1 agonists have attributes that would make either of them an appropriate choice in the management of all 3 patients in our case studies, while either DPP-4 inhibitor would be an appropriate choice for Case 1. Differences in dosing, administration, safety, and tolerability should be considered.

Distinguishing among incretin-based therapies. Patient education and self-management.

Working closely with patients and providing ongoing education, ideally in conjunction with a diabetes care team, can help ensure that the best treatment options are selected for an individual patient and that the patient is capable of effective self-management.

Distinguishing among incretin-based therapies. Safety, tolerability, and nonglycemic effects of incretin-based therapies.

The overall safety profiles of GLP-1 agonists and DPP-4 inhibitors are favorable, with a low incidence of hypoglycemia. This attribute, along with their weight and cardiovascular benefits, particularly with the GLP-1 agonists, make them appropriate choices in our 3 patient cases. Ongoing safety investigations with GLP-1 agonists and DPP-4 inhibitors will provide further clarity to the complete safety profiles of these agents.

Distinguishing among incretin-based therapies. Pathophysiology of type 2 diabetes mellitus: potential role of incretin-based therapies.

The multifactorial nature of the pathogenesis of T2DM provides an opportunity to combine treatments that act upon different mechanisms. In addition to improving insulin resistance and pancreatic ß-cell dysfunction, the GLP-1 agonists and DPP-4 inhibitors improve the impaired incretin response, as well as increase insulin secretion and reduce glucagon secretion, both in a glucose-dependent manner. As a result of these multiple actions, the GLP-1 agonists and DPP-4 inhibitors lower both fasting and postprandial glucose levels. The effects of GLP-1 agonists tend to be greater, probably because they produce pharmacologic levels of GLP-1 compared to physiologic levels with the DPP-4 inhibitors. Another difference is that unlike the DPP-4 inhibitors, the GLP-1 agonists also slow gastric emptying and promote satiety.

Distinguishing among incretin-based therapies. Introduction.

The "treat to target" approach is to quickly achieve the target glycosylated hemoglobin (AIC) goal of <7% in most people, and then intensify or change therapy as needed to maintain glycemic control. Results of an online survey demonstrate uncertainty regarding the clinical differences between glucagon-like peptide (GLP-1) agonists and dipeptidyl peptidase (DPP)-4 inhibitors. The increasingly important roles of the GLP-1 agonists and DPP-4 inhibitors stem from their overall good efficacy and safety profiles compared with other treatment options.

Practical applications of therapy with a glucagon-like peptide-1 receptor agonist.

Patients such as ML represent a common challenge in the primary care management of patients with T2DM. After some response to initial therapy with lifestyle management and metformin, the A1C goal of <7.0% after 2 to 3 months was not achieved, necessitating the initiation of combination therapy. The 4 groups of medications recommended by the ADA/EASD panel as the preferred therapies are basal insulin, the sulfonylureas, the TZD pioglitazone, and GLP-1 receptor agonists. In addition to considering efficacy, safety, cost, and other medication-related factors, the treatment plan must take into account the patient's individual needs, concerns, and capabilities. These additional considerations help to foster increased patient self-management and greater treatment adherence. To achieve these objectives, comprehensive patient education is essential. The unique mechanism of action of the GLP-1 receptor agonist class of medications makes these agents a desirable choice as add-on therapy to metformin.

Effectiveness of the novel anti-diabetes medication exenatide in everyday practice: Comparison with clinical trials.

We evaluated effectiveness of exenatide in 81 unselected patients and compared the results with clinical trials. Patients achieved a similar reduction in A1C and lost more weight than patients in the clinical trials. Thirty-seven (46%) were treated off-label. Exenatide was effective on and off-label in this unselected patient group.