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“Relative hypoglycemia” is an often-overlooked complication of diabetes characterized by an increase in the glycemic threshold for detecting and responding to hypoglycemia. The clinical relevance of this problem is linked to growing evidence that among patients with critical illness, higher blood glucose in the intensive care unit is associated with higher mortality among patients without diabetes but lower mortality in patients with preexisting diabetes and an elevated prehospitalization HbA1c. Although additional studies are needed, the cardiovascular stress associated with hypoglycemia perception, which can occur at normal or even elevated glucose levels in patients with diabetes, offers a plausible explanation for this difference in outcomes. Little is known, however, regarding how hypoglycemia is normally detected by the brain, much less how relative hypoglycemia develops in patients with diabetes. In this article, we explore the role in hypoglycemia detection played by glucose-responsive sensory neurons supplying peripheral vascular beds and/or circumventricular organs. These observations support a model wherein relative hypoglycemia results from diabetes-associated impairment of this neuronal glucose-sensing process. By raising the glycemic threshold for hypoglycemia perception, this impairment may contribute to the increased mortality risk associated with standard glycemic management of critically ill patients with diabetes.

The measurement of blood ketones in preference to urine ketones has become a well-established tool in the diagnosis and management of diabetic ketoacidosis (DKA). However, there remains considerable disparity between diabetes guidelines regarding if, how, and when this test should be used. While recent guidelines now mainly emphasize blood measurement, several issues nonetheless remain. Many laboratories still measure blood ketones using a semiquantitative test that does not measure the predominant ketone, β-hydroxybutyrate (BOHB), which may hinder patient management. Even when BOHB is measured, the evidence for cutoffs used in DKA diagnosis or exclusion is limited, while its use in gauging severity, treatment progress, and resolution is not fully clear. Lastly, although employing point-of-care meters instead of a laboratory for BOHB measurement brings undoubted benefits, this approach has its own challenges. This article provides a perspective on these topics to complement current recommendations and to suggest how future research may improve its use in the DKA context.

The ADA/EASD Precision Medicine in Diabetes Initiative (PMDI) was launched in 2018 by the American Diabetes Association (ADA), in collaboration with the European Association for the Study of Diabetes (EASD). The PMDI has subsequently partnered with other organizations including the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), the Juvenile Diabetes Research Foundation (JDRF) and the Diabetes Technology Society (DTS). The mandate of the PMDI is to establish consensus on the viability and ultimate clinical implementation of precision medicine for the diagnosis, prevention, treatment, prognosis, and monitoring of diabetes. This process is designed to take place through systematic evaluation of available scientific evidence, expert consultation, and broad stakeholder engagement (Figure 1). The mandate of the PMDI is pursued with the overarching goal of facilitating longer, healthier lives for people living with diabetes, identifying those at risk, and preventing diabetes through multiple interventional approaches. The PMDI held its first international scientific conference in Madrid, Spain, in October 2019. Approximately 100 delegates participated, with attendees from North America, Europe, Middle East, Asia, and Africa, with diverse representation (including academia, industry, funders, and people with diabetes). The conference provided the foundation and framework from which a first consensus paper on precision medicine in diabetes was developed and published in 2020 (1). The consensus paper named five key pillars, or domains, of precision diabetes medicine: Precision Diagnostics, Precision Prevention, Precision Treatment, Precision Prognostics, and Precision Monitoring. Across each domain, the paper also identified critical gaps in knowledge and evidence required for the scientific advancement, implementation, and ongoing evaluation of precision medicine in diabetes. Newly formed PMDI Working Groups (composed of ~175 internationally leading clinicians and researchers in precision diabetes medicine – (see Appendix)) have since initiated multiple, ongoing evidence-based evaluations in diabetes related to the five key domains. The second international scientific conference organised by the PMDI was held in April 2021 (www.pdm2021.org). This meeting, hosted virtually owing to the COVID-19 pandemic, included more than 3,000 participants from 67 countries. Building on the foundation and framework established at the first conference, diverse stakeholders in the development and implementation of precision diabetes medicine exchanged new data, ideas, and opinions. Over three days, the meeting delivered interactive state-of-the-science overviews of all key domains of precision diabetes medicine in research and practice. The PMDI Working Groups undertaking systematic reviews provided progress updates, summaries of existing evidence, and critical gaps in current scientific knowledge. Plenary presentations discussed the interface of precision medicine in diabetes with the social determinants of health and disparities in care, as well as the impact of racial discrimination and other forms of health inequity. Early career investigators showcased novel findings and contributions to the five key domains. Daily keynote lectures, delivered by people living with diabetes from around the globe, highlighted patient perspectives and offered insights into the opportunities for precision diabetes medicine to improve the day-to-day experience of living with diabetes. A moderated poster session, with abstracts selected by reviewers, was a focus of trainees and early-career investigators, while other abstracts were available for viewing online.

Since diabetes was first described over 3,000 years ago, clinicians and scientists alike have sought ever improving treatments en route to a cure. As we approach the 100th anniversary of insulin’s first therapeutic use, this article will recount the glorious history associated with research surrounding insulin’s isolation, purification, cloning, and subsequent modification. The discovery path we will relate tells the story of many relentless and passionate investigators pursuing ground-breaking research. The fruits of their labor include several Nobel Prizes, new technology, and, more importantly, ever improving treatments for one of humankind’s greatest medical scourges.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the “single disease” approach appears untenable, as does the notion of individualizing each single patient’s care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.

Randomized controlled trials (RCTs) have become the gold standard of clinical evidence and the staple of guided clinical practice. RCTs are based on a complex set of principles and procedures heavily strung by statistical analysis, primarily designed to answer a specific question in a clinical experiment. Readers of clinical trials need to apply critical appraisal skills before blindly accepting the results and conclusions of trials, lest they misinterpret and misapply the findings. We introduce the fundamentals of an RCT and discuss the relationship between relative risk (RR) and absolute risk (AR) in terms of the different information each conveys. The top results of some recent cardiovascular outcome trials using sodium–glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists in patients with type 2 diabetes are used to exemplify the merit of assessing both RR and AR changes for a balanced translation of findings into shrewd clinical judgment. We also suggest practical points to assist with a clinically useful interpretation of both within-trial and across-trial reports. Finally, we mention an alternative approach, namely, the restricted mean survival time, to obtaining unbiased estimates of the mean time of missed events in the treatment versus placebo arm for the duration of the trial.

Recent epidemiological data have shown that more than half of all new cases of type 1 diabetes occur in adults. Key genetic, immune, and metabolic differences exist between adult- and childhood-onset type 1 diabetes, many of which are not well understood. A substantial risk of misclassification of diabetes type can result. Notably, some adults with type 1 diabetes may not require insulin at diagnosis, their clinical disease can masquerade as type 2 diabetes, and the consequent misclassification may result in inappropriate treatment. In response to this important issue, JDRF convened a workshop of international experts in November 2019. Here, we summarize the current understanding and unanswered questions in the field based on those discussions, highlighting epidemiology and immunogenetic and metabolic characteristics of adult-onset type 1 diabetes as well as disease-associated comorbidities and psychosocial challenges. In adult-onset, as compared with childhood-onset, type 1 diabetes, HLA-associated risk is lower, with more protective genotypes and lower genetic risk scores; multiple diabetes-associated autoantibodies are decreased, though GADA remains dominant. Before diagnosis, those with autoantibodies progress more slowly, and at diagnosis, serum C-peptide is higher in adults than children, with ketoacidosis being less frequent. Tools to distinguish types of diabetes are discussed, including body phenotype, clinical course, family history, autoantibodies, comorbidities, and C-peptide. By providing this perspective, we aim to improve the management of adults presenting with type 1 diabetes.

The diagnosis of and criteria for gestational diabetes mellitus (GDM) continue to divide the scientific and medical community, both between and within countries. Many argue for universal adoption of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria and feel that further clinical trials are unjustified and even unethical. However, there are concerns about the large increase in number of women who would be diagnosed with GDM using these criteria and the subsequent impact on health care resources and the individual. This Perspective reviews the origins of the IADPSG consensus and points out some of its less well-known limitations, particularly with respect to identifying women at risk for an adverse pregnancy outcome. It also questions the clinical and cost-effectiveness data often cited to support the IADPSG glycemic thresholds. We present the argument that adoption of diagnostic criteria defining GDM should be based on response to treatment at different diagnostic thresholds of maternal glycemia. This will likely require an international multicenter trial of treatment.

The American Diabetes Association 2020 Standards of Medical Care in Diabetes (Standards of Care) recommends a hemoglobin A1c (A1C) of

Prevailing dietary guidelines have widely recommended diets relatively low in red and processed meats and high in minimally processed plant foods for the prevention of chronic diseases. However, an ad hoc research group called the Nutritional Recommendations (NutriRECS) consortium recently issued “new dietary guidelines” encouraging individuals to continue their current meat consumption habits due to “low certainty” of the evidence, difficulty of altering meat eaters’ habits and preferences, and the lack of need to consider environmental impacts of red meat consumption. These recommendations are not justified, in large part because of the flawed methodologies used to review and grade nutritional evidence. The evidence evaluation was largely based on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria, which are primarily designed to grade the strength of evidence for clinical interventions especially pharmacotherapy. However, the infeasibility for conducting large, long-term randomized clinical trials on most dietary, lifestyle, and environmental exposures makes the criteria inappropriate in these areas. A separate research group proposed a modified and validated system for rating the meta-evidence on nutritional studies (NutriGRADE) to address several limitations of the GRADE criteria. Applying NutriGRADE, the evidence on the positive association between red and processed meats and type 2 diabetes was rated to be of “high quality,” while the evidence on the association between red and processed meats and mortality was rated to be of “moderate quality.” Another important limitation is that inadequate attention was paid to what might be replacing red meat, be it plant-based proteins, refined carbohydrates, or other foods. In summary, the red/processed meat recommendations by NutriRECS suffer from important methodological limitations and involve misinterpretations of nutritional evidence. To improve human and planetary health, dietary guidelines should continue to emphasize dietary patterns low in red and processed meats and high in minimally processed plant foods such as fruits and vegetables, whole grains, nuts, and legumes.

Certain chronic comorbidities, including diabetes, are highly prevalent in people with coronavirus disease 2019 (COVID-19) and are associated with an increased risk of severe COVID-19 and mortality. Mild glucose elevations are also common in COVID-19 patients and associated with worse outcomes even in people without diabetes. Several studies have recently reported new-onset diabetes associated with COVID-19. The phenomenon of new-onset diabetes following admission to the hospital has been observed previously with other viral infections and acute illnesses. The precise mechanisms for new-onset diabetes in people with COVID-19 are not known, but it is likely that a number of complex interrelated processes are involved, including previously undiagnosed diabetes, stress hyperglycemia, steroid-induced hyperglycemia, and direct or indirect effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the β-cell. There is an urgent need for research to help guide management pathways for these patients. In view of increased mortality in people with new-onset diabetes, hospital protocols should include efforts to recognize and manage acute hyperglycemia, including diabetic ketoacidosis, in people admitted to the hospital. Whether new-onset diabetes is likely to remain permanent is not known, as the long-term follow-up of these patients is limited. Prospective studies of metabolism in the setting of postacute COVID-19 will be required to understand the etiology, prognosis, and treatment opportunities.

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

HbA1c is a valuable metric for comparing treatment groups in a randomized trial, for assessing glycemic trends in a population over time, or for cross-sectional comparisons of glycemic control in different populations. However, what is not widely appreciated is that HbA1c may not be a good indicator of an individual patient’s glycemic control because of the wide range of mean glucose concentrations and glucose profiles that can be associated with a given HbA1c level. To illustrate this point, we plotted mean glucose measured with continuous glucose monitoring (CGM) versus central laboratory–measured HbA1c in 387 participants in three randomized trials, showing that not infrequently HbA1c may underestimate or overestimate mean glucose, sometimes substantially. Thus, if HbA1c is to be used to assess glycemic control, it is imperative to know the patient’s actual mean glucose to understand how well HbA1c is an indicator of the patient’s glycemic control. With knowledge of the mean glucose, an estimated HbA1c (eA1C) can be calculated with the formula provided in this article to compare with the measured HbA1c. Estimating glycemic control from HbA1c alone is in essence applying a population average to an individual, which can be misleading. Thus, a patient’s CGM glucose profile has considerable value for optimizing his or her diabetes management. In this era of personalized, precision medicine, there are few better examples with respect to the fallacy of applying a population average to a specific patient rather than using specific information about the patient to determine the optimal approach to treatment.

Hyperglycemia is the major risk factor for microvascular complications in patients with type 2 diabetes (T2D). However, cardiovascular disease (CVD) is the principal cause of death, and lowering HbA1c has only a modest effect on reducing CVD risk and mortality. The recently published LEADER and SUSTAIN-6 trials demonstrate that, in T2D patients with high CVD risk, the glucagon-like peptide 1 receptor agonists liraglutide and semaglutide reduce the primary major adverse cardiac events (MACE) end point (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke) by 13% and 24%, respectively. The EMPA-REG OUTCOME, IRIS (subjects without diabetes), and PROactive (second principal end point) studies also demonstrated a significant reduction in cardiovascular events in T2D patients treated with empagliflozin and pioglitazone. However, the benefit of these four antidiabetes agents (liraglutide, semaglutide, empagliflozin, and pioglitazone) on the three individual MACE end points differed, suggesting that different underlying mechanisms were responsible for the reduction in cardiovascular events. Since liraglutide, semaglutide, pioglitazone, and empagliflozin similarly lower the plasma glucose concentration but appear to reduce CVD risk by different mechanisms, there emerges the intriguing possibility that, if used in combination, the effects of these antidiabetes agents may be additive or even multiplicative with regard to cardiovascular benefit.

Current efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody–positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes–associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes–associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.

Blood oligosaccharides are attached to many proteins after translation, forming glycoproteins. Glycosylation refers to an enzyme-mediated modification that alters protein function, for example, their life span or their interactions with other proteins (1). By contrast, glycation refers to a monosaccharide (usually glucose) attaching nonenzymatically to the amino group of a protein. Glycated hemoglobin is formed by the condensation of glucose with select amino acid residues, commonly lysine, in hemoglobin to form an unstable Schiff base (aldimine, pre-HbA1c) (Fig. 1). The Schiff base may dissociate or may undergo an Amadori rearrangement to form a stable ketoamine. Figure 1 Formation of glycated protein. A reversible interaction between a primary amino group (depicted as NH2) of a protein and the carbonyl group of d-glucose yields a labile intermediate, called a Schiff base. This can undergo a slow and spontaneous Amadori rearrangement to form a stable ketoamine. HbA1c is formed if glucose attaches to the N-terminal valine of the β-chain of hemoglobin. If the glucose attaches to proteins in the plasma, fructosamine or glycated albumin results. RBC, red blood cell. Glycated hemoglobin, particularly HbA1c, has for decades been widely incorporated into the management (and, more recently, the diagnosis) of patients with diabetes. An important attribute is that glycation occurs continuously over the lifetime of the protein, so the concentration of the glycated protein reflects the average blood glucose value over a period of time. This contrasts with the measurement of blood glucose, which reveals the glucose concentration at the instant blood is sampled and which is acutely altered by multiple factors such as hormones, illness, food ingestion, and exercise (2). While HbA1c is by far the most extensively used—and studied—glycated protein (2–4), other glycated proteins that have been evaluated in clinical studies include fructosamine, glycated albumin, and …

Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. In this Perspective, we focus on the acute manifestations of GV, particularly on hypoglycemia, and review measures assessing the amplitude of GV from routine self-monitored BG data, as well as its timing from continuous glucose monitoring (CGM) data. With availability of CGM, the latter is not only possible but also a requirement—we can now assess rapid glucose fluctuations in real time and relate their speed and magnitude to clinically relevant outcomes. Our primary message is that diabetes control is all about optimization and balance between two key markers—frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization, including to automated technologies such as the “artificial pancreas.” Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components—amplitude and timing.

What will it take to bring disease-modifying therapy to clinical use in type 1 diabetes? Coordinated efforts of investigators involved in discovery, translational, and clinical research operating in partnership with funders and industry and in sync with regulatory agencies are needed. This Perspective describes one such effort, Type 1 Diabetes TrialNet, a National Institutes of Health–funded and JDRF-supported international clinical trials network that emerged from the Diabetes Prevention Trial–Type 1 (DPT-1). Through longitudinal natural history studies, as well as trials before and after clinical onset of disease combined with mechanistic and ancillary investigations to enhance scientific understanding and translation to clinical use, TrialNet is working to bring disease-modifying therapies to individuals with type 1 diabetes. Moreover, TrialNet uses its expertise and experience in clinical studies to increase efficiencies in the conduct of trials and to reduce the burden of participation on individuals and families. Herein, we highlight key contributions made by TrialNet toward a revised understanding of the natural history of disease and approaches to alter disease course and outline the consortium’s plans for the future.

Objective This project explored lifestyles of patients in good and poor control to identify naturally occurring practices and strategies that result in successful diabetes management. Research design and methods Semistructured interviews with adult patients with type 2 diabetes explored diet, food preparation, physical activity, medication use and glucose monitoring. Patients (n=56) were classified into good (A1C 8.0%) control groups and matched across groups on diabetes duration (±5 years) and medication modality (none, oral, insulin±oral) to control for non-lifestyle factors. A qualitative comparative analysis identified practices that distinguished glycemic groups. Results Good control patients were more likely to test their glucose two or more times a day and reduce their sodium intake, as well as increase fruits and vegetables and limit portion sizes, some attaining good control without exercise. Fair control patients discussed several dietary strategies including limiting sweets, drinking non-caloric beverages, reducing carbs, ‘cheating’ (eating only a few sweets/limiting carbs in one meal to have more in another meal) and tested their glucose once a day. Poor control patients were more likely to skip antidiabetic medications and not test their glucose. Conclusions Although clinical trials indicate most self-management practices have limited effectiveness over time, increased glucose monitoring is a valuable component in daily management. Research is needed on effectiveness of dietary strategies that emphasize sodium monitoring and allow some degree of cheating. Reoffering diabetes education classes and providing pill boxes as memory aids may help improve poor control.

Objectives Gestational diabetes mellitus (GDM) greatly increases the risk of developing diabetes in the decade after delivery, but few women receive appropriately timed postpartum glucose testing (PPGT) or a referral to primary care (PC) for continued monitoring. This qualitative study was designed to identify barriers and facilitators to testing and referral from patient and providers' perspectives. Methods We interviewed patients and clinicians in depth about knowledge, values, priorities, challenges, and recommendations for increasing PPGT rates and PC linkage. Interviews were coded with NVIVO data analysis software, and analyzed using an implementation science framework. Results Women reported motivation to address GDM for the health of the fetus. Most women did not anticipate future diabetes for themselves, and focused on delivery outcomes rather than future health risks. Patients sought and received reassurance from clinicians, and were unlikely to discuss early onset following GDM or preventive measures. PPGT barriers described by patients included provider not mentioning the test or setting it up, transportation difficulties, work responsibilities, fatigue, concerns about fasting while breastfeeding, and timing of the test after discharge from obstetrics, and no referral to PC for follow-up. Practitioners described limited communication among multiple care providers during pregnancy and delivery, systems issues, and separation of obstetrics from PC. Conclusions Patients' barriers to PPGT included low motivation for self-care, structural obstacles, and competing priorities. Providers reported the need to balance risk with reassurance, and identified systems failures related to test timing, limitations of electronic medical record systems (EMR), lack of referrals to PC, and inadequate communication between specialties. Prevention of early onset has great potential for medical cost savings and improvements in quality of life.

Objective: The primary aim was to evaluate the extent to which persons with type 1 diabetes perform selfmonitoring of blood glucose (SMBG) according to guidelines. Secondary objectives were to investigate predictors for good SMBG adherence, reasons for non-adherence, and association between SMBG frequency and hemoglobin A1c (HbA1c). Methods: This was a survey-based cross-sectional study. Questionnaires were sent out to 600 random patients at five sites. Patients were included if they were diagnosed with type 1 diabetes and ?18 years old and excluded if they were currently using continuous glucose monitoring (CGM). Analysis of data was performed separately for the three sites where the answer frequency was ?70%. Results: In total, 138 of 314 study participants, 43.9% (95% CI 38.5% to 49.4%) performed SMBG ?4 times per day. For the three clinics where ?70% of surveyed patients were included in the analysis, results were similar, 41.3% (95% CI 34.7% to 47.8%). Top three reported reasons for not performing more frequent SMBG were lack of time, not remembering, and self-consciousness. Frequency of SMBG was associated with HbA1c levels (p

Objective The goal of this study was to describe medication usage patterns in patients with type 2 diabetes mellitus (T2DM) initiating treatment with non-insulin antidiabetic drugs (NIADs), basal insulin, or prandial/mixed insulin using real-world data. Research design and methods A retrospective analysis using the Truven Health MarketScan Research Databases was conducted to identify adults (≥18 years) with T2DM from 2006 to 2012. Patients were categorized into four cohorts based on diabetes treatment. Cohort 1 (n=597 664) consisted of newly diagnosed patients who did not receive any treatment, cohort 2 (n=342 511) included NIAD initiators, cohort 3 (n=99 578) included basal insulin initiators, and cohort 4 (n=62 876) included prandial/mixed insulin initiators. Patients transitioned out of a cohort once they met the criteria for the next one. Results Patients in cohort 2 were younger (56.2 years, SD±12.1) than patients in cohorts 1, 3, and 4 (58 years, SD±0.75). Metformin was the most commonly prescribed drug in cohort 2 patients. Basal insulin usage decreased from 71% in year 1 to 47% in year 4, in cohort 3 patients. Approximately one-third of these patients switched to prandial/mixed insulin each year. In cohort 4, the usage of prandial/mixed insulin decreased to 61% by year 4. Use of basal insulin and NIAD remained common in this group. Mean glycosylated hemoglobin (HbA1c) values decreased by ∼1% for each of the treatment cohorts following treatment initiation and remained stable during follow-up. All-cause and diabetes-related medical costs were highest for patients in cohorts 3 and 4. Conclusions Overall, our findings demonstrate that treatment intensification was low in all study cohorts despite elevated HbA1c levels during preindex and follow-up period.

Objective Patients with diabetes are at increased risk of foot ulcers, which may result in limb amputations. While regular foot care prevents ulcerations and amputation in those patients with diabetes not on dialysis, evidence is limited in diabetic hemodialysis patients. We investigated the association between the implementation of a routine foot check program in diabetic incident hemodialysis patients, and major lower limb amputations. Methods In 1/2008, monthly intradialytic foot checks were implemented as part of standard clinic care in all Fresenius Medical Care North America hemodialysis facilities. Patients with diabetes who initiated hemodialysis between 1/2004 and 12/2007 constituted the preimplementation cohort, and patients starting hemodialysis between 1/2008 and 12/2011 comprised the postimplementation cohort. In addition, we conducted a sensitivity analysis where we excluded patients from the clinics with

Context Diabetic muscle infarction (DMI) is a rare complication associated with poorly controlled diabetes mellitus. Less than 200 cases have been reported in the literature since it was first described over 45 years ago. There is no clear ‘standard of care’ for managing these patients. Evidence acquisition PubMed searches were conducted for ‘diabetic muscle infarction’ and ‘diabetic myonecrosis’ from database inception through July 2014. All articles identified by these searches were reviewed in detail if the article text was available in English. Evidence synthesis The current literature exists as case reports or small case series, with no prospective or higher-order treatment studies available. Thus, an evidence-based approach to data synthesis was difficult. The available literature is presented objectively with an attempt to describe clinically relevant trends and findings in the diagnosis and management of DMI. Conclusions Early recognition of DMI is key, so appropriate treatment can be initiated. MRI is the radiological study of choice. A combination of bed rest, glycemic control, and non-steroidal anti-inflammatory drug therapy appears to yield the shortest time to symptom resolution and the lowest risk of recurrence.