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New advances in type 1 diabetes

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This article has a correction. Please see:

• New advances in type 1 diabetes - June 03, 2024
• Savitha Subramanian , professor of medicine ,
• Farah Khan , clinical associate professor of medicine ,
• Irl B Hirsch , professor of medicine
• University of Washington Diabetes Institute, Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, WA, USA
• Correspondence to: I B Hirsch ihirsch{at}uw.edu

Type 1 diabetes is an autoimmune condition resulting in insulin deficiency and eventual loss of pancreatic β cell function requiring lifelong insulin therapy. Since the discovery of insulin more than 100 years ago, vast advances in treatments have improved care for many people with type 1 diabetes. Ongoing research on the genetics and immunology of type 1 diabetes and on interventions to modify disease course and preserve β cell function have expanded our broad understanding of this condition. Biomarkers of type 1 diabetes are detectable months to years before development of overt disease, and three stages of diabetes are now recognized. The advent of continuous glucose monitoring and the newer automated insulin delivery systems have changed the landscape of type 1 diabetes management and are associated with improved glycated hemoglobin and decreased hypoglycemia. Adjunctive therapies such as sodium glucose cotransporter-1 inhibitors and glucagon-like peptide 1 receptor agonists may find use in management in the future. Despite these rapid advances in the field, people living in under-resourced parts of the world struggle to obtain necessities such as insulin, syringes, and blood glucose monitoring essential for managing this condition. This review covers recent developments in diagnosis and treatment and future directions in the broad field of type 1 diabetes.

Introduction

Type 1 diabetes is an autoimmune condition that occurs as a result of destruction of the insulin producing β cells of the pancreatic islets, usually leading to severe endogenous insulin deficiency. 1 Without treatment, diabetic ketoacidosis will develop and eventually death will follow; thus, lifelong insulin therapy is needed for survival. Type 1 diabetes represents 5-10% of all diabetes, and diagnosis classically occurs in children but can also occur in adulthood. The burden of type 1 diabetes is expansive; it can result in long term complications, decreased life expectancy, and reduced quality of life and can add significant financial burden. Despite vast improvements in insulin, insulin delivery, and glucose monitoring technology, a large proportion of people with type 1 diabetes do not achieve glycemic goals. The massive burden of type 1 diabetes for patients and their families needs to be appreciated. The calculation and timing of prandial insulin dosing, often from food with unknown carbohydrate content, appropriate food and insulin dosing when exercising, and cost of therapy are all major challenges. The psychological realities of both acute management and the prospect of chronic complications add to the burden. Education programs and consistent surveillance for “diabetes burnout” are ideally available to everyone with type 1 diabetes.

In this review, we discuss recent developments in the rapidly changing landscape of type 1 diabetes and highlight aspects of current epidemiology and advances in diagnosis, technology, and management. We do not cover the breadth of complications of diabetes or certain unique scenarios including psychosocial aspects of type 1 diabetes management, management aspects specific to older adults, and β cell replacement therapies. Our review is intended for the clinical reader, including general internists, family practitioners, and endocrinologists, but we acknowledge the critical role that people living with type 1 diabetes and their families play in the ongoing efforts to understand this lifelong condition.

Sources and selection criteria

We did individual searches for studies on PubMed by using terms relevant to the specific topics covered in this review pertaining to type 1 diabetes. Search terms used included “type 1 diabetes” and each individual topic—diagnosis, autoantibodies, adjuvant therapies, continuous glucose monitoring, automated insulin delivery, immunotherapies, diabetic ketoacidosis, hypoglycemia, and under-resourced settings. We considered all studies published in the English language between 1 January 2001 and 31 January 2023. We selected publications outside of this timeline on the basis of relevance to each topic. We also supplemented our search strategy by a hand search of the references of key articles. We prioritized studies on each highlighted topic according to the level of evidence (randomized controlled trials (RCTs), systematic reviews and meta-analyses, consensus statements, and high quality observational studies), study size (we prioritized studies with at least 50 participants when available), and time of publication (we prioritized studies published since 2003 except for the landmark Diabetes Control and Complications Trial and a historical paper by Tuomi on diabetes autoantibodies, both from 1993). For topics on which evidence from RCTs was unavailable, we included other study types of the highest level of evidence available. To cover all important clinical aspects of the broad array of topics covered in this review, we included additional publications such as clinical reviews as appropriate on the basis of clinical relevance to both patients and clinicians in our opinion.

Epidemiology

The incidence of type 1 diabetes is rising worldwide, possibly owing to epigenetic and environmental factors. Globally in 2020 an estimated 8.7 million people were living with type 1 diabetes, of whom approximately 1.5 million were under 20 years of age. 2 This number is expected to rise to more than 17 million by 2040 ( https://www.t1dindex.org/#global ). The International Diabetes Federation estimates the global prevalence of type 1 diabetes at 0.1%, and this is likely an underestimation as diagnoses of type 1 diabetes in adults are often not accounted for. The incidence of adult onset type 1 diabetes is higher in Europe, especially in Nordic countries, and lowest in Asian countries. 3 Adult onset type 1 diabetes is also more prevalent in men than in women. An increase in prevalence in people under 20 years of age has been observed in several western cohorts including the US, 4 5 Netherlands, 6 Canada, 7 Hungary, 8 and Germany. 9

Classically, type 1 diabetes presents over the course of days or weeks in children and adolescents with polyuria, polydipsia, and weight loss due to glycosuria. The diagnosis is usually straightforward, with profound hyperglycemia (often >300 mg/dL) usually with ketonuria with or without ketoacidemia. Usually, more than one autoantibody is present at diagnosis ( table 1 ). 10 The number of islet autoantibodies combined with parameters of glucose tolerance now forms the basis of risk prediction for type 1 diabetes, with stage 3 being clinical disease ( fig 1 ). 11 The originally discovered autoantibody, islet cell antibody, is no longer used clinically owing to variability of the assay despite standardisation. 12

Autoantibody characteristics associated with increased risk of type 1 diabetes 10

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Natural history of type 1 diabetes. Adapted with permission from Insel RA, et al. Diabetes Care 2015;38:1964-74 11

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Half of all new cases of type 1 diabetes are now recognized as occurring in adults. 13 Misclassification due to misdiagnosis (commonly as type 2 diabetes) occurs in nearly 40% of people. 14 As opposed to typical childhood onset type 1 diabetes, progression to severe insulin deficiency, and therefore its clinical presentation in adults, is variable. The term latent autoimmune diabetes of adults (LADA) was introduced 30 years ago to identify adults who developed immune mediated diabetes. 15 An international consensus defined the diagnostic criteria for LADA as age >30 years, lack of need for insulin use for at least six months, and presence of islet cell autoantibodies. 16 However, debate as to whether the term LADA should even be used as a diagnostic term persists. The American Diabetes Association (ADA) Standards of Care note that for the purpose of classification, all forms of diabetes mediated by autoimmune β cell destruction are included in the classification of type 1 diabetes. 17 Nevertheless, they note that use of the term LADA is acceptable owing to the practical effect of heightening awareness of adults likely to have progressive autoimmune β cell destruction and thereby accelerating insulin initiation by clinicians to prevent diabetic ketoacidosis.

The investigation of adults with suspected type 1 diabetes is not always straightforward ( fig 2 ). 18 Islet cell autoantibodies such as glutamic acid decarboxylase antibody (GADA), tyrosine phosphatase IA2 antibody, and zinc transporter isoform 8 autoantibody act as markers of immune activity and can be detected in the blood with standardized assays ( table 1 ). The presence of one or more antibodies in adults with diabetes could mark the progression to severe insulin deficiency; these individuals should be considered to have type 1 diabetes. 1 Autoantibodies, especially GADA, should be measured only in people with clinically suspected type 1 diabetes, as low concentrations of GADA can be seen in type 2 diabetes and thus false positive measurements are a concern. 19 That 5-10% of cases of type 1 diabetes may occur without diabetes autoantibodies is also now clear, 20 and that the diabetes autoantibodies disappear over time is also well appreciated. 21

Flowchart for investigation of suspected type 1 diabetes in adults, based on data from white European populations. No single clinical feature in isolation confirms type 1 diabetes. The most discriminative feature is younger age at diagnosis (<35 years), with lower body mass index (<25), unintentional weight loss, ketoacidosis, and glucose >360 mg/dL at presentation. Adapted with permission from Holt RIG, et al. Diabetes Care 2021;44:2589-625 1

Genetic risk scoring (GRS) for type 1 diabetes has received attention to differentiate people whose classification is unclear. 22 23 24 Developed in 2019, the T1D-GRS2 uses 67 single nucleotide polymorphisms from known autoimmune loci and can predict type 1 diabetes in children of European and African ancestry. Although GRS is not available for routine clinical use, it may allow prediction of future cases of type 1 diabetes to allow prevention strategies with immune intervention (see below).

A major change in the type 1 diabetes phenotype has occurred over the past few decades, with an increase in obesity; the reasons for this are complex. In the general population, including people with type 1 diabetes, an epidemic of sedentary lifestyles and the “westernized diet” consisting of increased processed foods, refined sugars, and saturated fat is occurring. In people with type 1 diabetes, the overall improvement in glycemic control since the report of the Diabetes Control and Complications Trial (DCCT) in 1993 (when one or two insulin injections a day was standard therapy) has resulted in less glycosuria so that the typical patient with lower body weight is uncommon in high income countries. In the US T1D Exchange, more than two thirds of the adult population were overweight or obese. 25

Similarly, obesity in young people with type 1 diabetes has also increased over the decades. 26 The combination of autoimmune insulin deficiency with obesity and insulin resistance has received several descriptive names over the years, with this phenotype being described as double diabetes and hybrid diabetes, among others, 26 27 but no formal nomenclature in the diabetes classification exists. Many of these patients have family members with type 2 diabetes, and some patients probably do have both types of diabetes. Clinically, minimal research has been done into how this specific population responds to certain antihyperglycemic oral agents, such as glucagon-like peptide 1 (GLP-1) receptor agonists, given the glycemic, weight loss, and cardiovascular benefits seen with these agents. 28 These patients are common in most adult diabetes practices, and weight management in the presence of insulin resistance and insulin deficiency remains unclear.

Advances in monitoring

The introduction of home blood glucose monitoring (BGM) more than 45 years ago was met with much skepticism until the report of the DCCT. 29 Since then, home BGM has improved in accuracy, precision, and ease of use. 30 Today, in many parts of the world, home BGM, a static measurement of blood glucose, has been replaced by continuous glucose monitoring (CGM), a dynamic view of glycemia. CGM is superior to home BGM for glycemic control, as confirmed in a meta-analysis of 21 studies and 2149 participants with type 1 diabetes in which CGM use significantly decreased glycated hemoglobin (HbA 1c ) concentrations compared with BGM (mean difference −0.23%, 95% confidence interval −3.83 to −1.08; P<0.001), with a greater benefit if baseline HbA 1c was >8% (mean difference −0.43%, −6.04 to −3.30; P<0.001). 31 This newer technology has also evolved into a critical component of automated insulin delivery. 32

CGM is the standard for glucose monitoring for most adults with type 1 diabetes. 1 This technology uses interstitial fluid glucose concentrations to estimate blood glucose. Two types of CGM are available. The first type, called “real time CGM”, provides a continuous stream of glucose data to a receiver, mobile application, smartwatch, or pump. The second type, “intermittently scanned CGM,” needs to be scanned by a reader device or smartphone. Both of these technologies have shown improvements in HbA 1c and amount of time spent in the hypoglycemic range compared with home BGM when used in conjunction with multiple daily injections or “open loop” insulin pump therapy. 33 34 Real time CGM has also been shown to reduce hypoglycemic burden in older adults with type 1 diabetes ( table 2 ). 36 Alerts that predict or alarm with both hypoglycemia and hyperglycemia can be customized for the patient’s situation (for example, a person with unawareness of hypoglycemia would have an alert at a higher glucose concentration). Family members can also remotely monitor glycemia and be alerted when appropriate. The accuracy of these devices has improved since their introduction in 2006, so that currently available sensors can be used without a confirmation glucose concentration to make a treatment decision with insulin. However, some situations require home BGM, especially when concerns exist that the CGM does not match symptoms of hypoglycemia.

Summary of trials for each topic covered

Analysis of CGM reports retrospectively can assist therapeutic decision making both for the provider and the patient. Importantly, assessing the retrospective reports and watching the CGM in real time together offer insight to the patient with regard to insulin dosing, food choices, and exercise. Patients should be encouraged to assess their data on a regular basis to better understand their diabetes self-management. Table 3 shows standard metrics and targets for CGM data. 52 Figure 3 shows an ambulatory glucose profile.

Standardized continuous glucose monitoring metrics for adults with diabetes 52

Example of ambulatory glucose profile of 52 year old woman with type 1 diabetes and fear of hypoglycemia. CGM=continuous glucose monitoring; GMI=glucose management indicator

Improvements in technology and evidence for CGM resulting in international recommendations for its widespread use have resulted in greater uptake by people with type 1 diabetes across the globe where available and accessible. Despite this, not everyone wishes to use it; some people find wearing any device too intrusive, and for many the cost is prohibitive. These people need at the very least before meal and bedtime home BGM.

A next generation implantable CGM device (Sensionics), with an improved calibration algorithm that lasts 180 days after insertion by a healthcare professional, is available in both the EU and US. Although fingerstick glucose calibration is needed, the accuracy is comparable to that of other available devices. 53

Advances in treatments

The discovery of insulin in 1921, resulting in a Nobel Prize, was considered one of the greatest scientific achievements of the 20th century. The development of purified animal insulins in the late 1970s, followed by human insulin in the early 1980s, resulted in dramatic reductions in allergic reactions and lipoatrophy. Introduction of the first generation of insulin analogs, insulin lispro in the mid-1990s followed by insulin glargine in the early 2000s, was an important advance for the treatment of type 1 diabetes. 54 We review the next generation of insulin analogs here. Table 4 provides details on available insulins.

Pharmacokinetics of commonly used insulin preparations

Ultra-long acting basal insulins

Insulin degludec was developed with the intention of improving the duration of action and achieving a flatter profile compared with the original long acting insulin analogs, insulin glargine and insulin detemir. Its duration of action of 42 hours at steady state means that the profile is generally flat without significant day-to-day variability, resulting in less hypoglycemia compared with U-100 glargine. 39 55

When U-100 insulin glargine is concentrated threefold, its action is prolonged. 56 U-300 glargine has a different kinetic profile and is delivered in one third of the volume of U-100 glargine, with longer and flatter effects. The smaller volume of U-300 glargine results in slower and more gradual release of insulin monomers owing to reduced surface area in the subcutaneous space. 57 U-300 glargine also results in lesser hypoglycemia compared with U-100 glargine. 58

Ultra-rapid acting prandial insulins

Rapid acting insulin analogs include insulin lispro, aspart, and glulisine. With availability of insulin lispro, the hope was for a prandial insulin that better matched food absorption. However, these newer insulins are too slow to control the glucose spike seen with ingestion of a high carbohydrate load, leading to the development of insulins with even faster onset of action.

The first available ultra-rapid prandial insulin was fast acting insulin aspart. This insulin has an onset of appearance approximately twice as fast (~5 min earlier) as insulin aspart, whereas dose-concentration and dose-response relations are comparable between the two insulins ( table 4 ). 59 In adults with type 1 diabetes, mealtime and post-meal fast acting aspart led to non-inferior glycemic control compared with mealtime aspart, in combination with basal insulin. 60 Mean HbA 1c was 7.3%, 7.3%, and 7.4% in the mealtime faster aspart, mealtime aspart, and post‐meal faster aspart arms, respectively (P<0.001 for non-inferiority).

Insulin lispro-aabc is the second ultra-rapid prandial insulin. In early kinetic studies, insulin lispro-aabc appeared in the serum five minutes faster with 6.4-fold greater exposure in the first 15 minutes compared with insulin lispro. 61 The duration of exposure of the insulin concentrations in this study was 51 minutes faster with lispro-aabc. Overall insulin exposure was similar between the two groups. Clinically, lispro-aabc is non-inferior to insulin lispro, but postprandial hyperglycemia is lower with the faster acting analog. 62 Lispro-aabc given at mealtime resulted in greater improvement in post-prandial glucose (two hour post-prandial glucose −31.1 mg/dL, 95% confidence interval −41.0 to −21.2; P<0.001).

Both ultra-rapid acting insulins can be used in insulin pumps. Lispro-aabc tends to have more insertion site reactions than insulin lispro. 63 A meta-analysis including nine studies and 1156 participants reported increased infusion set changes on rapid acting insulin analogs (odds ratio 1.60, 95% confidence interval 1.26 to 2.03). 64

Pulmonary inhaled insulin

The quickest acting insulin is pulmonary inhaled insulin, with an onset of action of 12 minutes and a duration of 1.5-3 hours. 65 When used with postprandial supplemental dosing, glucose control is improved without an increase in hypoglycemia. 66

Insulin delivery systems

Approved automated insulin delivery systems.

CGM systems and insulin pumps have shown improvement in glycemic control and decreased risk of severe hypoglycemia compared with use of self-monitoring of blood glucose and multiple daily insulin injections in type 1 diabetes. 67 68 69 Using CGM and insulin pump together (referred to as sensor augmented pump therapy) only modestly improves HbA 1c in patients who have high sensor wear time, 70 71 but the management burden of diabetes does not decrease as frequent user input is necessary. Thus emerged the concept of glucose responsive automated insulin delivery (AID), in which data from CGM can inform and allow adjustment of insulin delivery.

In the past decade, exponential improvements in CGM technologies and refined insulin dosing pump algorithms have led to the development of AID systems that allow for minimization of insulin delivery burden. The early AID systems reduced hypoglycemia risk by automatically suspending insulin delivery when glucose concentrations dropped to below a pre-specified threshold but did not account for high glucose concentrations. More complex algorithms adjusting insulin delivery up and down automatically in response to real time sensor glucose concentrations now allow close replication of normal endocrine pancreatic physiology.

AID systems (also called closed loop or artificial pancreas systems) include three components—an insulin pump that continuously delivers rapid acting insulin, a continuous glucose sensor that measures interstitial fluid glucose at frequent intervals, and a control algorithm that continuously adjusts insulin delivery that resides in the insulin pump or a smartphone application or handheld device ( fig 4 ). All AID systems that are available today are referred to as “hybrid” closed loop (HCL) systems, as users are required to manually enter prandial insulin boluses and signal exercise, but insulin delivery is automated at night time and between meals. AID systems, regardless of the type used, have shown benefit in glycemic control and cost effectiveness, improve quality of life by improving sleep quality, and decrease anxiety and diabetes burden in adults and children. 72 73 74 Limitations to today’s HCL systems are primarily related to pharmacokinetics and pharmacodynamics of available analog insulins and accuracy of CGM in extremes of blood glucose values. The iLet bionic pancreas, cleared by the US Food and Drug Administration (FDA) in May 2023, is an AID system that determines all therapeutic insulin doses for an individual on the basis of body weight, eliminating the need for calculation of basal rates, insulin to carbohydrate ratios, blood glucose corrections, and bolus dose. The control algorithms adapt continuously and autonomously to the individual’s insulin needs. 38 Table 5 lists available AID systems.

Schematic of closed loop insulin pump technology. The continuous glucose monitor senses interstitial glucose concentrations and sends the information via Bluetooth to a control algorithm hosted on an insulin pump (or smartphone). The algorithm calculates the amount of insulin required, and the insulin pump delivers rapid acting insulin subcutaneously

Comparison of commercially available hybrid closed loop systems 75

Unapproved systems

Do-it-yourself (DIY) closed loop systems—DIY open artificial pancreas systems—have been developed by people with type 1 diabetes with the goal of self-adjusting insulin by modifying their individually owned devices. 76 These systems are built by the individual using an open source code widely available to anyone with compatible medical devices who is willing and able to build their own system. DIY systems are used by several thousand people across the globe but are not approved by regulatory bodies; they are patient-driven and considered “off-label” use of technology with the patient assuming full responsibility for their use. Clinicians caring for these patients should ensure basic diabetes skills, including pump site maintenance, a knowledge of how the chosen system works, and knowing when to switch to “manual mode” for patients using an artificial pancreas system of any kind. 76 The small body of studies on DIY looping suggests improvement in HbA 1c , increased time in range, decreased hypoglycemia and glucose variability, improvement in night time blood glucose concentrations, and reduced mental burden of diabetes management. 77 78 79 Although actively prescribing or initiating these options is not recommended, these patients should be supported by clinical teams; insulin prescription should not be withheld, and, if initiated by the patient, unregulated DIY options should be openly discussed to ensure open and transparent relationships. 78

In January 2023, the US FDA cleared the Tidepool Loop app, a DIY AID system. This software will connect the CGM, insulin pump, and Loop algorithm, but no RCTs using this method are available.

β cell replacement therapies

For patients with type 1 diabetes who meet specific clinical criteria, β cell replacement therapy using whole pancreas or pancreatic islet transplantation can be considered. Benefits of transplantation include immediate cessation of insulin therapy, attainment of euglycemia, and avoidance of hypoglycemia. Additional benefits include improved quality of life and stabilization of complications. 80 Chronic immunosuppression is needed to prevent graft rejection after transplantation.

Pancreas transplantation

Whole pancreas transplantation, first performed in 1966, involves complex abdominal surgery and lifelong immunosuppressive therapy and is limited by organ donor availability. Today, pancreas transplants are usually performed simultaneously using two organs from the same donor (simultaneous pancreas-kidney transplant (SPKT)), sequentially if the candidate has a living donor for renal transplantation (pancreas after kidney transplant (PAKT)) or on its own (pancreas transplantation alone). Most whole pancreas transplants are performed with kidney transplantation for end stage diabetic kidney disease. Pancreas graft survival at five years after SPKT is 80% and is superior to that with pancreas transplants alone (62%) or PAKT (67%). 81 Studies from large centers where SPKT is performed show that recipients can expect metabolic improvements including amelioration of problematic hypoglycemia for at least five years. 81 The number of pancreas transplantations has steadily decreased in the past two decades.

Islet transplantation

Islet transplantation can be pursued in selected patients with type 1 diabetes marked by unawareness of hypoglycemia and severe hypoglycemic episodes, to help restore the α cell response critical for responding to hypoglycemia. 82 83 Islet transplantation involves donor pancreas procurement with subsequent steps to isolate, purify, culture, and infuse the islets. Multiple donors are needed to provide enough islet cells to overcome islet cell loss during transplantation. Survival of the islet grafts, limited donor supply, and lifelong need for immunosuppressant therapy remain some of the biggest challenges. 84 Islet transplantation remains experimental in the US and is offered in a few specialized centers in North America, some parts of Europe, and Australia. 85

Disease modifying treatments for β cell preservation

Therapies targeting T cells, B cells, and cytokines that find use in a variety of autoimmune diseases have also been applied to type 1 diabetes. The overarching goal of immune therapies in type 1 diabetes is to prevent or delay the loss of functional β cell mass. Studies thus far in early type 1 diabetes have not yet successfully shown reversal of loss of C peptide or maintenance of concentrations after diagnosis, although some have shown preservation or slowing of loss of β cells. This suggests that a critical time window of opportunity exists for starting treatment depending on the stage of type 1 diabetes ( fig 1 ).

Teplizumab is a humanized monoclonal antibody against the CD3 molecule on T cells; it is thought to modify CD8 positive T lymphocytes, key effector cells that mediate β cell death and preserves regulatory T cells. 86 Teplizumab, when administered to patients with new onset of type 1 diabetes, was unable to restore glycemia despite C peptide preservation. 87 However, in its phase II prevention study of early intervention in susceptible individuals (at least two positive autoantibodies and an abnormal oral glucose tolerance test at trial entry), a single course of teplizumab delayed progression to clinical type 1 diabetes by about two years ( table 2 ). 43 On the basis of these results, teplizumab received approval in the US for people at high risk of type 1 diabetes in November 2022. 88 A phase III trial (PROTECT; NCT03875729 ) to evaluate the efficacy and safety of teplizumab versus placebo in children and adolescents with new diagnosis of type 1 diabetes (within six weeks) is ongoing. 89

Thus far, targeting various components of the immune response has been attempted in early type 1 diabetes without any long term beneficial effects on C peptide preservation. Co-stimulation blockade using CTLA4-Ig abatacept, a fusion protein that interferes with co-stimulation needed in the early phases of T cell activation that occurs in type 1 diabetes, is being tested for efficacy in prevention of type 1 diabetes ( NCT01773707 ). 90 Similarly, several cytokine directed anti-inflammatory targets (interleukin 6 receptor, interleukin 1β, tumor necrosis factor ɑ) have not shown any benefit.

Non-immunomodulatory adjunctive therapies

Adjunctive therapies for type 1 diabetes have been long entertained owing to problems surrounding insulin delivery, adequacy of glycemic management, and side effects associated with insulin, especially weight gain and hypoglycemia. At least 50% of adults with type 1 diabetes are overweight or obese, presenting an unmet need for weight management in these people. Increased cardiovascular risk in these people despite good glycemic management presents additional challenges. Thus, use of adjuvant therapies may tackle these problems.

Metformin, by decreasing hepatic glucose production, could potentially decrease fasting glucose concentrations. 91 It has shown benefit in reducing insulin doses and possibly improving metabolic control in obese/overweight people with type 1 diabetes. A meta-analysis of 19 RCTs suggests short term improvement in HbA 1c that is not sustained after three months and is associated with higher incidence of gastrointestinal side effects. 92 No evidence shows that metformin decreases cardiovascular morbidity in type 1 diabetes. Therefore, owing to lack of conclusive benefit, addition of metformin to treatment regimens is not recommended in consensus guidelines.

Glucagon-like peptide receptor agonists

Endogenous GLP-1 is an incretin hormone secreted from intestinal L cells in response to nutrient ingestion and enhances glucose induced insulin secretion, suppresses glucagon secretion, delays gastric emptying, and induces satiety. 93 GLP-1 promotes β cell proliferation and inhibits apoptosis, leading to expansion of β cell mass. GLP-1 secretion in patients with type 1 diabetes is similar to that seen in people without diabetes. Early RCTs of liraglutide in type 1 diabetes resulted in weight loss and modest lowering of HbA 1c ( table 2 ). 49 50 Liraglutide 1.8 mg in people with type 1 diabetes and higher body mass index decreased HbA 1c , weight, and insulin requirements with no increased hypoglycemia risk. 94 However, on the basis of results from a study of weekly exenatide that showed similar results, these effects may not be sustained. 51 A meta-analysis of 24 studies including 3377 participants showed that the average HbA 1c decrease from GLP-1 receptor agonists compared with placebo was highest for liraglutide 1.8 mg daily (−0.28%, 95% confidence interval −0.38% to−0.19%) and exenatide (−0.17%, −0.28% to 0.02%). The estimated weight loss from GLP-1 receptor agonists compared with placebo was −4.89 (−5.33 to−4.45)  kg for liraglutide 1.8 mg and −4.06  (−5.33 to−2.79) kg for exenatide. 95 No increase in severe hypoglycemia was seen (odds ratio 0.67, 0.43 to 1.04) but therapy was associated with higher levels of nausea. GLP-1 receptor agonist use may be beneficial for weight loss and reducing insulin doses in a subset of patients with type 1 diabetes. GLP-1 receptor agonists are not a recommended treatment option in type 1 diabetes. Semaglutide is being studied in type 1 diabetes in two clinical trials ( NCT05819138 ; NCT05822609 ).

Sodium-glucose cotransporter inhibitors

Sodium-glucose cotransporter 2 (SGLT-2), a protein expressed in the proximal convoluted tubule of the kidney, reabsorbs filtered glucose; its inhibition prevents glucose reabsorption in the tubule and increases glucose excretion by the kidney. Notably, the action of these agents is independent of insulin, so this class of drugs has potential as adjunctive therapy for type 1 diabetes. Clinical trials have shown significant benefit in cardiovascular and renal outcomes in type 2 diabetes; therefore, significant interest exists for use in type 1 diabetes. Several available SGLT-2 inhibitors have been studied in type 1 diabetes and have shown promising results with evidence of decreased total daily insulin dosage, improvement in HbA 1c , lower rates of hypoglycemia, and decrease in body weight; however, these effects do not seem to be sustained at one year in clinical trials and seem to wane with time. Despite beneficial effects, increased incidence of diabetic ketoacidosis has been observed in all trials, is a major concern, and is persistent despite educational efforts. 96 97 98 Low dose empagliflozin (2.5 mg) has shown lower rates of diabetic ketoacidosis in clinical trials ( table 2 ). 47 Favorable risk profiles have been noted in Japan, the only market where SGLT-2 inhibitors are approved for adjunctive use in type 1 diabetes. 99 In the US, SGLT-2 inhibitors are approved for use in type 2 diabetes only. In Europe, although dapagliflozin was approved for use as adjunct therapy to insulin in adults with type 1 diabetes, the manufacturer voluntarily withdrew the indication for the drug in 2021. 100 Sotagliflozin is a dual SGLT-1 and SGLT-2 inhibitor that decreases renal glucose reabsorption through systemic inhibition of SGLT-2 and decreases glucose absorption in the proximal intestine by SGLT-1 inhibition, blunting and delaying postprandial hyperglycemia. 101 Studies of sotagliflozin in type 1 diabetes have shown sustained HbA 1c reduction, weight loss, lower insulin requirements, lesser hypoglycemia, and more diabetic ketoacidosis relative to placebo. 102 103 104 The drug received authorization in the EU for use in type 1 diabetes, but it is not marketed there. Although SGLT inhibitors are efficacious in type 1 diabetes management, the risk of diabetic ketoacidosis is a major limitation to widespread use of these agents.

Updates in acute complications of type 1 diabetes

Diabetic ketoacidosis.

Diabetic ketoacidosis is a serious and potentially fatal hyperglycemic emergency accompanied by significant rates of mortality and morbidity as well as high financial burden for healthcare systems and societies. In the past decade, increasing rates of diabetic ketoacidosis in adults have been observed in the US and Europe. 105 106 This may be related to changes in the definition of diabetic ketoacidosis, use of medications associated with higher risk, and admission of patients at lower risk. 107 In a US report of hospital admissions with diabetic ketoacidosis, 53% of those admitted were between the ages of 18 and 44, with higher rates in men than in women. 108 Overall, although mortality from diabetic ketoacidosis in developed countries remains low, rates have risen in people aged >60 and in those with coexisting life threatening illnesses. 109 110 Recurrent diabetic ketoacidosis is associated with a substantial mortality rate. 111 Frequency of diabetic ketoacidosis increases with higher HbA 1c concentrations and with lower socioeconomic status. 112 Common precipitating factors include newly diagnosed type 1 diabetes, infection, poor adherence to insulin, and an acute cardiovascular event. 109

Euglycemic diabetic ketoacidosis refers to the clinical picture of an increased anion gap metabolic acidosis, ketonemia, or significant ketonuria in a person with diabetes without significant glucose elevation. This can be seen with concomitant use of SGLT-2 inhibitors (currently not indicated in type 1 diabetes), heavy alcohol use, cocaine use, pancreatitis, sepsis, and chronic liver disease and in pregnancy 113 Treatment is similar to that for hyperglycemic diabetic ketoacidosis but can require earlier use and greater concentrations of a dextrose containing fluid for the insulin infusion in addition to 0.9% normal saline resuscitation fluid. 114

The diagnosis of diabetic ketoacidosis has evolved from a gluco-centric diagnosis to one requiring hyperketonemia. By definition, independent of blood glucose, a β-hydroxybutyrate concentration >3 mmol/L is required for diagnosis. 115 However, the use of this ketone for assessment of the severity of the diabetic ketoacidosis is controversial. 116 Bedside β-hydroxybutyrate testing during treatment is standard of care in many parts of the world (such as the UK) but not others (such as the US). Concerns have been raised about accuracy of bedside β-hydroxybutyrate meters, but this is related to concentrations above the threshold for diabetic ketoacidosis. 116

Goals for management of diabetic ketoacidosis include restoration of circulatory volume, correction of electrolyte imbalances, and treatment of hyperglycemia. Intravenous regular insulin infusion is the standard of care for treatment worldwide owing to rapidity of onset of action and rapid resolution of ketonemia and hyperglycemia. As hypoglycemia and hypokalemia are more common during treatment, insulin doses are now recommended to be reduced from 0.1 u/kg/h to 0.05 u/kg/h when glucose concentrations drop below 250 mg/dL or 14 mM. 115 Subcutaneous rapid acting insulin protocols have emerged as alternative treatments for mild to moderate diabetic ketoacidosis. 117 Such regimens seem to be safe and have the advantages of not requiring admission to intensive care, having lower rates of complications related to intravenous therapy, and requiring fewer resources. 117 118 Ketonemia and acidosis resolve within 24 hours in most people. 115 To prevent rebound hyperglycemia, the transition off an intravenous insulin drip must overlap subcutaneous insulin by at least two to four hours. 115

Hypoglycemia

Hypoglycemia, a common occurrence in people with type 1 diabetes, is a well appreciated effect of insulin treatment and occurs when blood glucose falls below the normal range. Increased susceptibility to hypoglycemia from exogenous insulin use in people with type 1 diabetes results from multiple factors, including imperfect subcutaneous insulin delivery tools, loss of glucagon within a few years of diagnosis, progressive impairment of the sympatho-adrenal response with repeated hypoglycemic episodes, and eventual development of impaired awareness. In 2017 the International Hypoglycemia Study Group developed guidance for definitions of hypoglycemia; on the basis of this, a glucose concentration of 3.0-3.9 mmol/L (54-70 mg/dL) was designated as level 1 hypoglycemia, signifying impending development of level 2 hypoglycemia—a glucose concentration <3 mmol/L (54 mg/dL). 119 120 At approximately 54 mg/dL, neuroglycopenic hypoglycemia symptoms, including vision and behavior changes, seizures, and loss of consciousness, begin to occur as a result of glucose deprivation of neurons in the central nervous system. This can eventually lead to cerebral dysfunction at concentrations <50 mg/dL. 121 Severe hypoglycemia (level 3), denoting severe cognitive and/or physical impairment and needing external assistance for recovery, is a common reason for emergency department visits and is more likely to occur in people with lower socioeconomic status and with the longest duration of diabetes. 112 Prevalence of self-reported severe hypoglycemia is very high according to a global population study that included more than 8000 people with type 1 diabetes. 122 Severe hypoglycemia occurred commonly in younger people with suboptimal glycemia according to a large electronic health record database study in the US. 123 Self- reported severe hypoglycemia is associated with a 3.4-fold increase in mortality. 124 125

Acute consequences of hypoglycemia include impaired cognitive function, temporary focal deficits including stroke-like symptoms, and memory deficits. 126 Cardiovascular effects including tachycardia, arrhythmias, QT prolongation, and bradycardia can occur. 127 Hypoglycemia can impair many activities of daily living, including motor vehicle safety. 128 In a survey of adults with type 1 diabetes who drive a vehicle at least once a week, 72% of respondents reported having hypoglycemia while driving, with around 5% reporting a motor vehicle accident due to hypoglycemia in the previous two years. 129 This contributes to the stress and fear that many patients face while grappling with the difficulties of ongoing hypoglycemia. 130

Glucagon is highly efficacious for the primary treatment of severe hypoglycemia when a patient is unable to ingest carbohydrate safely, but it is unfortunately under-prescribed and underused. 131 132 Availability of nasal, ready to inject, and shelf-stable liquid glucagon formulations have superseded the need for reconstituting older injectable glucagon preparations before administration and are now preferred. 133 134 Real time CGM studies have shown a decreased hypoglycemic exposure in people with impaired awareness without a change in HbA 1c . 34 135 136 137 138 CGM has shown benefit in decreasing hypoglycemia across the lifespan, including in teens, young adults, and older people. 36 139 Although CGM reduces the burden of hypoglycemia including severe hypoglycemia, it does not eliminate it; overall, such severe level 3 hypoglycemia rates in clinical trials are very low and hard to decipher in the real world. HCL insulin delivery systems integrated with CGM have been shown to decrease hypoglycemia. Among available rapid acting insulins, ultra-rapid acting lispro (lispro-aabc) seems to be associated with less frequent hypoglycemia in type 1 diabetes. 140 141

As prevention of hypoglycemia is a crucial aspect of diabetes management, formal training programs to increase awareness and education on avoidance of hypoglycemia, such as the UK’s Dose Adjustment for Normal Eating (DAFNE), have been developed. 142 143 This program has shown fewer severe hypoglycemia (mean 1.7 (standard deviation 8.5) episodes per person per year before training to 0.6 (3.7) episodes one year after training) and restoration of recognition of hypoglycemia in 43% of people reporting unawareness. Clinically relevant anxiety and depression fell from 24.4% to 18.0% and from 20.9% to 15.5%, respectively. A structured education program with cognitive and psychotherapeutic aspects for changing hypoglycemia related behaviors, called the Hypoglycemia Awareness Restoration Program despite optimized self-care (HARPdoc), showed a positive effect on changing unhelpful beliefs around hypoglycemia and improved diabetes related and general distress and anxiety scores. 144

Management in under-resourced settings

According to a recent estimate from the International Diabetes Federation, 1.8 million people with type 1 diabetes live in low and middle income countries (LMICs). 2 In many LMICs, the actual burden of type 1 diabetes remains unknown and material resources needed to manage type 1 diabetes are lacking. 145 146 Health systems in these settings are underequipped to tackle the complex chronic disease that is type 1 diabetes. Few diabetes and endocrinology specialist physicians are available owing to lack of specific postgraduate training programs in many LMICs; general practitioners with little to no clinical experience in managing type 1 diabetes care for these patients. 146 This, along with poor availability and affordability of insulin and lack of access to technology, results in high mortality rates. 147 148 149 In developed nations, low socioeconomic status is associated with higher levels of mortality and morbidity for adults with type 1 diabetes despite access to a universal healthcare system. 150 Although global governments have committed to universal health coverage and therefore widespread availability of insulin, it remains very far from realization in most LMICs. 151

Access to technology is patchy and varies globally. In the UST1DX, CGM use was least in the lowest fifth of socioeconomic status. 152 Even where technology is available, successful engagement does not always occur. 153 In a US cohort, lower CGM use was seen in non-Hispanic Black children owing to lower rates of device initiation and higher rates of discontinuation. 154 In many LMICs, blood glucose testing strips are not readily available and cost more than insulin. 151 In resource limited settings, where even diagnosis, basic treatments including insulin, syringes, and diabetes education are limited, use of CGM adds additional burden to patients. Need for support services and the time/resources needed to download and interpret data are limiting factors from a clinician’s perspective. Current rates of CGM use in many LMICs are unknown.

Inequities in the availability of and access to certain insulin formulations continue to plague diabetes care. 155 In developed countries such as the US, rising costs have led to insulin rationing by around 25% of people with type 1 diabetes. 156 LMICs have similar trends while also remaining burdened by disproportionate mortality and complications from type 1 diabetes. 155 157 With the inclusion of long acting insulin analogs in the World Health Organization’s Model List of Essential Medicines in 2021, hope has arisen that these will be included as standard of care across the world. 158 In the past, the pricing of long acting analogs has limited their use in resource poor settings 159 ; however, their inclusion in WHO’s list was a major step in improving their affordability. 158 With the introduction of lower cost long acting insulin biosimilars, improved access to these worldwide in the future can be anticipated. 160

Making insulin available is not enough on its own to improve the prognosis for patients with diabetes in resource poor settings. 161 Improved healthcare infrastructure, better availability of diabetes supplies, and trained personnel are all critical to improving type 1 diabetes care in LMICs. 161 Despite awareness of limitations and barriers, a clear understanding of how to implement management strategies in these settings is still lacking. The Global Diabetes Compact was launched in 2021 with the goal of increasing access to treatment and improving outcomes for people with diabetes across the globe. 162

Emerging technologies and treatments

Monitoring systems.

The ability to measure urinary or more recently blood ketone concentrations is an integral part of self-management of type 1 diabetes, especially during acute illness, intermittent fasting, and religious fasts to prevent diabetic ketoacidosis. 163 Many people with type 1 diabetes do not adhere to urine or blood ketone testing, which likely results in unnecessary episodes of diabetic ketoacidosis. 164 Noting that blood and urine ketone testing is not widely available in all countries and settings is important. 1 Regular assessment of patients’ access to ketone testing (blood or urine) is critical for all clinicians. Euglycemic diabetic ketoacidosis in type 1 diabetes is a particular problem with concomitant use of SGLT-2 inhibitors; for this reason, these agents are not approved for use in these patients. For sick day management (and possibly for the future use of SGLT-2 inhibitors in people with type 1 diabetes), it is hoped that continuous ketone monitoring (CKM) can mitigate the risks of diabetic ketoacidosis. 165 Like CGM, the initial CKM device measures interstitial fluid β-hydroxybutyrate instead of glucose. CKM use becomes important in conjunction with a hybrid closed loop insulin pump system and added SGLT-2 inhibitor therapy, where insulin interruptions are common and hyperketonemia is frequent. 166

Perhaps the greatest technological challenge to date has been the development of non-invasive glucose monitoring. Numerous attempts have been made using strategies including optics, microwave, and electrochemistry. 167 Lack of success to date has resulted in healthy skepticism from the medical community. 168 However, active interest in the development of non-invasive technology with either interstitial or blood glucose remains.

Insulin and delivery systems

In the immediate future, two weekly basal insulins, insulin icodec and basal insulin Fc, may become available. 169 Studies of insulin icodec in type 1 diabetes are ongoing (ONWARDS 6; NCT04848480 ). How these insulins will be incorporated in management of type 1 diabetes is not yet clear.

Currently available AID systems use only a single hormone, insulin. Dual hormone AID systems incorporating glucagon are in development. 170 171 Barriers to the use of dual hormone systems include the need for a second chamber in the pump, a lack of stable glucagon formulations approved for long term subcutaneous delivery, lack of demonstrated long term safety, and gastrointestinal side effects from glucagon use. 74 Similarly, co-formulations of insulin and amylin (a hormone co-secreted with insulin and deficient in people with type 1 diabetes) are in development. 172

Immunotherapy for type 1 diabetes

As our understanding of the immunology of type 1 diabetes expands, development of the next generation of immunotherapies is under active pursuit. Antigen specific therapies, peptide immunotherapy, immune tolerance using DNA vaccination, and regulatory T cell based adoptive transfer targeting β cell senescence are all future opportunities for drug development. Combining immunotherapies with metabolic therapies such as GLP-1 receptor agonists to help to improve β cell mass is being actively investigated.

The quest for β cell replacement methods is ongoing. Transplantation of stem cell derived islets offers promise for personalized regenerative therapies as a potentially curative method that does away with the need for donor tissue. Since the first in vivo model of glucose responsive β cells derived from human embryonic stem cells, 173 different approaches have been attempted. Mesenchymal stromal cell treatment and autologous hematopoietic stem cells in newly diagnosed type 1 diabetes may preserve β cell function without any safety signals. 174 175 176 Stem cell transplantation for type 1 diabetes remains investigational. Encapsulation, in which β cells are protected using a physical barrier to prevent immune attack and avoid lifelong immunosuppression, and gene therapy techniques using CRISPR technology also remain in early stages of investigation.

Until recently, no specific guidelines for management of type 1 diabetes existed and management guidance was combined with consensus statements developed for type 2 diabetes. Table 6 summarizes available guidance and statements from various societies. A consensus report for management of type 1 diabetes in adults by the ADA and European Association for the Study of Diabetes became available in 2021; it covers several topics of diagnosis and management of type 1 diabetes, including glucose monitoring, insulin therapy, and acute complications. Similarly, the National Institute for Health and Care Excellence also offers guidance on management of various aspects of type 1 diabetes. Consensus statements for use of CGM, insulin pump, and AID systems are also available.

Guidelines in type 1 diabetes

Conclusions

Type 1 diabetes is a complex chronic condition with increasing worldwide prevalence affecting several million people. Several successes in management of type 1 diabetes have occurred over the years from the serendipitous discovery of insulin in 1921 to blood glucose monitoring, insulin pumps, transplantation, and immunomodulation. The past two decades have seen advancements in diagnosis, treatment, and technology including development of analog insulins, CGM, and advanced insulin delivery systems. Although we have gained a broad understanding on many important aspects of type 1 diabetes, gaps still exist. Pivotal research continues targeting immune targets to prevent or delay onset of type 1 diabetes. Although insulin is likely the oldest of existing modern drugs, no low priced generic supply of insulin exists anywhere in the world. Management of type 1 diabetes in under resourced areas continues to be a multifaceted problem with social, cultural, and political barriers.

Glossary of abbreviations

ADA—American Diabetes Association

AID—automated insulin delivery

BGM—blood glucose monitoring

CGM—continuous glucose monitoring

CKM—continuous ketone monitoring

DCCT—Diabetes Control and Complications Trial

DIY—do-it-yourself

FDA—Food and Drug Administration

GADA—glutamic acid decarboxylase antibody

GLP-1—glucagon-like peptide 1

GRS—genetic risk scoring

HbA1c—glycated hemoglobin

HCL—hybrid closed loop

LADA—latent autoimmune diabetes of adults

LMIC—low and middle income country

PAKT—pancreas after kidney transplant

RCT—randomized controlled trial

SGLT-2—sodium-glucose cotransporter 2

SPKT—simultaneous pancreas-kidney transplant

Questions for future research

What future new technologies can be helpful in management of type 1 diabetes?

How can newer insulin delivery methods benefit people with type 1 diabetes?

What is the role of disease modifying treatments in prevention and delay of type 1 diabetes?

Is there a role for sodium-glucose co-transporter inhibitors or glucagon-like peptide 1 receptor angonists in the management of type 1 diabetes?

As the population with type 1 diabetes ages, how should management of these people be tailored?

How can we better serve people with type 1 diabetes who live in under-resourced settings with limited access to medications and technology?

How patients were involved in the creation of this manuscript

A person with lived experience of type 1 diabetes reviewed a draft of the manuscript and offered input on important aspects of their experience that should be included. This person is involved in large scale education and activism around type 1 diabetes. They offered their views on various aspects of type 1 diabetes, especially the use of adjuvant therapies and the burden of living with diabetes. This person also raised the importance of education of general practitioners on the various stages of type 1 diabetes and the management aspects. On the basis of this feedback, we have highlighted the burden of living with diabetes on a daily basis.

Series explanation: State of the Art Reviews are commissioned on the basis of their relevance to academics and specialists in the US and internationally. For this reason they are written predominantly by US authors

Contributors: SS and IBH contributed to the planning, drafting, and critical review of this manuscript. FNK contributed to the drafting of portions of the manuscript. All three authors are responsible for the overall content as guarantors.

Competing interests: We have read and understood the BMJ policy on declaration of interests and declare the following interests: SS has received an honorarium from Abbott Diabetes Care; IBH has received honorariums from Abbott Diabetes Care, Lifescan, embecta, and Hagar and research support from Dexcom and Insulet.

Provenance and peer review: Commissioned; externally peer reviewed.

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Study provides preliminary evidence in favor of a new type 1 diabetes treatment

Type 1 diabetes is an autoimmune disease that causes the body's immune system to attack and destroy insulin-producing beta cells in the pancreas. Traditional management of type 1 diabetes has primarily involved replacing the missing insulin with injections which, though effective, can be expensive and burdensome. A new study led by researchers at the University of Chicago Medicine and Indiana University suggests that an existing drug could be repurposed to treat type 1 diabetes, potentially reducing dependence on insulin as the sole treatment.

The research centers on a medication known as α-difluoromethylornithine (DFMO), which inhibits an enzyme that plays a key role in cellular metabolism. The latest translational results are a culmination of years of research: In 2010, while corresponding author Raghu Mirmira, MD, PhD , was at Indiana University, he and his lab performed fundamental biochemistry experiments on beta cells in culture. They found that suppressing the metabolic pathway altered by DFMO helped protect the beta cells from environmental factors, hinting at the possibility of preserving and even restoring these vital cells in patients diagnosed with type 1 diabetes.

The researchers confirmed their observations preclinically in zebrafish and then in mice before senior author Linda DiMeglio, MD, MPH, Edwin Letzter Professor of Pediatrics at Indiana University School of Medicine and a pediatric endocrinologist at Riley Children's Health, launched a clinical trial to evaluate the safety and tolerability of the drug in type 1 diabetes patients. The results of the trial, which was funded by the Juvenile Diabetes Research Foundation (JDRF) and used DMFO provided by Panbela Therapeutics, indicated that the drug is safe for type 1 diabetes patients and can help keep insulin levels stable by protecting beta cells.

“As a physician-scientist, this is the kind of thing we’ve always strived for – to discover something at a very basic, fundamental level in cells and find a way to bring it into the clinic,” said Mirmira, who is now Professor of Medicine and an endocrinologist at UChicago Medicine. “It definitely underscores the importance of supporting basic science research.”

"It's been truly thrilling to witness the promising results in the pilot trial after this long journey, and we're excited to continue our meaningful collaboration," said DiMeglio.

Importantly, DFMO has already been FDA-approved as a high dose injection since 1990 for treating African Sleeping Sickness and received breakthrough therapy designation for neuroblastoma maintenance therapy after remission in 2020. Pre-existing regulatory approval could potentially facilitate its use in type 1 diabetes, saving effort and expense and getting the treatment to patients sooner.

“For a drug that’s already approved for other indications, the approval timeline can be a matter of years instead of decades once you have solid clinical evidence for safety and efficacy,” said Mirmira. “Using a new formulation of DFMO as a pill allows patients to take it by mouth instead of needing to undergo regular injections, and it has a very favorable side effect profile. It’s exciting to say we have a drug that works differently from every other treatment we have for this disease.”

To follow up on the recently published results, first and co-corresponding author Emily K. Sims, MD, Associate Professor of Pediatrics at IU School of Medicine and a pediatric endocrinologist at Riley Children's Health, launched a multi-center clinical trial, also funded by JDRF – with UChicago among the trial sites – to gather even stronger data regarding the efficacy of DFMO as a type 1 diabetes treatment.

"With our promising early findings, we hold hope that DFMO, possibly as part of a combination therapy, could offer potential benefits to preserve insulin secretion in individuals with recent-onset type 1 diabetes and ultimately also be tested in those who are at risk of developing the condition," said Sims.

“A new era is dawning where we’re thinking of novel ways to modify the disease using different types of drugs and targets that we didn’t classically think of in type 1 diabetes treatment,” said Mirmira.

The study, “Inhibition of Polyamine Biosynthesis Preserves β-Cell Function in Type 1 Diabetes,” was published in Cell Medicine Reports in November 2023. Co-authors include Emily K. Sims, Abhishek Kulkarni, Audrey Hull, Stephanie E. Woerner, Susanne Cabrera, Lucy D. Mastrandrea, Batoul Hammoud, Soumyadeep Sarkar, Ernesto S. Nakayasu, Teresa L. Mastracci, Susan M. Perkins, Fangqian Ouyang, Bobbie-Jo Webb-Robertson, Jacob R. Enriquez, Sarah A. Tersey, Carmella Evans-Molina, S. Alice Long, Lori Blanchfield, Eugene W. Gerner, Raghavendra Mirmira, and Linda A. DiMeglio.

Scientists advance type 1 diabetes treatment with cutting-edge stem cell and gene editing technologies

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Unlocking the potential of renewable β cells and precision gene editing, researchers aim to revolutionize diabetes care and bring us closer to a functional cure for type 1 diabetes.

In a recent study published in the journal Nature Reviews Endocrinology , researchers examined the progress in cell replacement therapies for type 1 diabetes mellitus (T1DM), focusing on generating replenishable β cells, improving transplantation methods, and addressing challenges related to immune modulation and clinical application.

T1DM affects 8.75 million people globally, approximately 1.52 million patients under 20. T1DM results from the autoimmune destruction of pancreatic β cells, consequent insulin insufficiency, and chronic hyperglycemia. Although glucose monitoring and insulin dosing help manage the disease, achieving optimal glycemic control remains challenging. Pancreatic islet or β cell transplantation offers a potential cure but faces challenges such as limited donor availability, poor cell engraftment, and the need for lifelong immunosuppression. Current research focuses on improving cell delivery, finding alternative cell sources, and reducing reliance on immunosuppression. In the present review, researchers discussed the current advancements in cell transplantation for T1DM, focusing on β cell generation, delivery technologies, immune modulation, relevant animal models, and the clinical translation of these therapies.

Renewable islet cell sources

The limited availability of donor islets has driven the development of stem cell-derived islets as a renewable source for T1DM therapy. These islets, generated from human pluripotent stem cells (hPSCs), show promise in clinical trials but remain challenged by functional immaturity, transcriptional identity issues, and the inability to control the ratio of β, α, and δ cells. During in vitro differentiation, a significant number of cells may acquire an unwanted identity, resembling serotonin-producing enterochromaffin cells, which complicates their application in T1DM therapy. While in vivo transplantation can enhance the function of these cells, optimizing in vitro production processes and ensuring safety, particularly concerning uncommitted cell types that could form tumors and ensuring genetic stability, remains crucial.  Advances in scalable manufacturing, characterization protocols, and cryopreservation will be essential for the clinical adoption and accessibility of these therapies.

Cell delivery strategies

Pancreatic islet transplantation for T1DM involves strategies like microencapsulation and macroencapsulation to protect islet cells and enhance their function. Microencapsulation encloses cells in gel-like microspheres, allowing nutrient exchange while shielding them from immune attacks. However, challenges like inflammation and fibrotic overgrowth affect long-term viability. Macroencapsulation delivers larger cell doses in retrievable units but faces issues with oxygen supply and fibrosis. Open devices and scaffolds aim for direct vascularization of grafts to improve integration and function, using approaches like simultaneous implant-transplant methods, decellularized tissue scaffolds, and 3D-printed architectures. Prevascularization systems are also explored to establish a vascular network before cell transplantation, improving cell survival and reducing immune responses. Despite these innovations, ensuring adequate mass transfer of oxygen, glucose, and insulin within encapsulation devices and managing graft size and immune protection remain significant hurdles. While these approaches show promise, challenges remain in achieving long-term efficacy , minimizing immune rejection, and optimizing oxygen and nutrient delivery to transplanted cells.

Alternative immunoprotection methods

β cell replacement faces challenges distinct from non-autoimmune diseases, mainly due to the need to prevent autoimmunity recurrence. Current immunosuppressive therapies are effective but have severe side effects, including organ toxicity and increased infection risks. Emerging strategies focus on more targeted, less toxic immunomodulation. These include biomaterial-based localized drug delivery, islet co-delivery with immunomodulatory cells, and reducing islet graft immunogenicity through advanced gene editing techniques. Biomaterials can deliver immunomodulatory drugs directly to the transplant site, while co-delivery with cells like mesenchymal stem cells improves islet survival. Gene editing technologies, such as CRISPR–Cas9, are being utilized to engineer hypoimmune islet grafts by knocking down immunogenic markers or overexpressing protective signals.  However, the long-term impact of these genetic modifications remains uncertain, and safety concerns persist regarding the potential for immune evasion by these modified cells.

Animal models

Animal models support the development of cell transplantation strategies and immunomodulatory interventions. Immunocompromised models, mainly using mice, allow for studying human islet engraftment without rejection. In contrast, immunocompetent models, such as rats, pigs, and non-human primates (NHPs), better mimic human immune responses critical for evaluating inflammatory and immune protection strategies. Humanized models, incorporating human immune components, provide a unique platform to assess the immunogenicity of β cell grafts and therapeutic interventions despite limitations such as graft-versus-host disease and shorter experimental timeframes. Pigs provide insights into islet transplantation due to their physiological similarities to humans, while NHPs serve as valuable translational models, contributing to understanding immune responses and developing new immunosuppressive strategies. Together, these models facilitate comprehensive assessments of therapeutic interventions for T1DM.

Clinical translation

Harmonizing preclinical testing protocols is vital for β cell replacement therapy development. Characterization of stem cell-derived β cells should include composition and functional assessments. Initial rodent studies must evaluate cell delivery and immune responses, with further validation in larger animal models. Development of a β cell replacement product involves renewable cell sources, effective delivery, and immune rejection prevention. The goal is to create a safe, reproducible product that restores glycemic control for over ten years without systemic immunosuppression.

Conclusion and outlook

In conclusion, cell transplantation for T1DM has evolved significantly, with stem cell-derived islets showing promise for clinical application. However, challenges related to cell composition, functional maturity, and long-term safety continue to be critical areas of focus. Collaborative consortia are accelerating progress by integrating complementary technologies. Innovations in renewable β cell sources, gene editing technology, and subcutaneous transplantation methods aim to improve cell delivery, immune modulation, and patient outcomes. The goal is to develop a widely applicable, practical, and accessible treatment that improves the quality of life for T1DM patients.

• Harnessing cellular therapeutics for type 1 diabetes mellitus: progress, challenges, and the road ahead. Grattoni, A. et al., Nature Reviews Endocrinology (2024), DOI: 10.1038/s41574-024-01029-0, https://www.nature.com/articles/s41574-024-01029-0

Posted in: Medical Science News | Medical Research News | Medical Condition News

Tags: Autoimmunity , Cas9 , Cell , Chronic , CRISPR , Diabetes , Diabetes Mellitus , Drug Delivery , Drugs , Efficacy , Endocrinology , Fibrosis , Gene , Genetic , Glucose , Hyperglycemia , Immunomodulatory , Immunosuppression , in vitro , in vivo , Inflammation , Insulin , Manufacturing , Mesenchymal Stem Cells , Oxygen , Preclinical , Preclinical Testing , Research , Serotonin , Stem Cells , Technology , Translation , Transplant , Type 1 Diabetes , Vascular

Dr. Sushama R. Chaphalkar

Dr. Sushama R. Chaphalkar is a senior researcher and academician based in Pune, India. She holds a PhD in Microbiology and comes with vast experience in research and education in Biotechnology. In her illustrious career spanning three decades and a half, she held prominent leadership positions in academia and industry. As the Founder-Director of a renowned Biotechnology institute, she worked extensively on high-end research projects of industrial significance, fostering a stronger bond between industry and academia.  

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Harnessing cellular therapeutics for type 1 diabetes mellitus: progress, challenges, and the road ahead

Affiliations.

• 1 Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, USA. [email protected].
• 2 Department of Surgery, Houston Methodist Hospital, Houston, TX, USA. [email protected].
• 3 Department of Radiation Oncology, Houston Methodist Hospital, Houston, TX, USA. [email protected].
• 4 Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada.
• 5 Department of Surgery, University of Alberta, Edmonton, Alberta, Canada.
• 6 Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
• 7 Department of Biomedical Engineering, University of Miami, Miami, FL, USA.
• 8 Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
• 9 Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, USA.
• 10 Woodruff School of Mechanical Engineering and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
• 11 J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA.
• 12 Diabetes Institute, University of Florida, Gainesville, FL, USA.
• 13 Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA, USA.
• 14 Department of Surgery, The University of Arizona, Tucson, AZ, USA.
• 15 Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
• 16 Department of Pediatrics, Ellis Fischel Cancer Center, School of Medicine, University of Missouri, Columbia, MO, USA.
• 17 Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St. Louis, MO, USA.
• 18 Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
• 19 Department of Cardiac Surgery, Boston Children's Hospital, Boston, MA, USA.
• 20 Department of Surgery, Harvard Medical School, Boston, MA, USA.
• 21 Harvard Stem Cell Institute, Cambridge, MA, USA.
• 22 Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
• 23 Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA.
• 24 Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
• 25 Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada.
• 26 Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA.
• 27 Department of Bioengineering, Rice University, Houston, TX, USA.
• 28 University of California, San Francisco, Department of Bioengineering and Therapeutic Sciences, San Francisco, CA, USA.
• 29 Brown University, School of Engineering, Providence, RI, USA.
• 30 McEwen Stem Cell Institute, University Health Network, Toronto, ON, Canada.
• 31 Department of Physiology, University of Toronto, Toronto, ON, Canada.
• 32 Advocacy Department, Breakthrough T1D, Washington, DC, USA.
• 33 Department of Medicine, Columbia Center for Translational Immunology, Columbia University, New York, NY, USA.
• 34 Department of Microbiology and Immunology, Columbia University, New York, NY, USA.
• 35 Department of Surgery, Columbia University, New York, NY, USA.
• 36 Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, USA.
• 37 UW Health Transplant Center, Madison, WI, USA.
• 38 Division of Transplantation, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
• 39 Diabetes Center, University of California San Francisco, San Francisco, CA, USA.
• 40 Department of Surgery, University of California San Francisco, San Francisco, CA, US.
• 41 Gladstone Institute of Genomic Immunology, University of California San Francisco, San Francisco, CA, USA.
• 42 Research Department, Breakthrough T1D, New York, NY, USA.
• 43 Research Department, Breakthrough T1D, New York, NY, USA. [email protected].
• 44 Vaccine and Immunotherapy Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. [email protected].
• 45 Immunoendocrinology, Division of Medical Biology, Department of Pathology and Medical Biology, University of Groningen and University Medical Center Groningen, Groningen, Netherlands. [email protected].
• PMID: 39227741
• DOI: 10.1038/s41574-024-01029-0

Type 1 diabetes mellitus (T1DM) is a growing global health concern that affects approximately 8.5 million individuals worldwide. T1DM is characterized by an autoimmune destruction of pancreatic β cells, leading to a disruption in glucose homeostasis. Therapeutic intervention for T1DM requires a complex regimen of glycaemic monitoring and the administration of exogenous insulin to regulate blood glucose levels. Advances in continuous glucose monitoring and algorithm-driven insulin delivery devices have improved the quality of life of patients. Despite this, mimicking islet function and complex physiological feedback remains challenging. Pancreatic islet transplantation represents a potential functional cure for T1DM but is hindered by donor scarcity, variability in harvested cells, aggressive immunosuppressive regimens and suboptimal clinical outcomes. Current research is directed towards generating alternative cell sources, improving transplantation methods, and enhancing cell survival without chronic immunosuppression. This Review maps the progress in cell replacement therapies for T1DM and outlines the remaining challenges and future directions. We explore the state-of-the-art strategies for generating replenishable β cells, cell delivery technologies and local targeted immune modulation. Finally, we highlight relevant animal models and the regulatory aspects for advancing these technologies towards clinical deployment.

© 2024. Springer Nature Limited.

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• International Diabetes Federation. IDF Diabetes Atlas Reports: Type 1 Diabetes Estimates in Children and Adults (International Diabetes Federation, 2022).
• US Centers for Disease Control and Prevention. National Diabetes Statistics Report (CDC, 2024).
• Tonnies, T. et al. Projections of type 1 and type 2 diabetes burden in the U.S. population aged <20 years through 2060: the SEARCH for diabetes in youth study. Diabetes Care 46, 313–320 (2023). - PubMed - DOI
• Syed, F. Z. Type 1 diabetes mellitus. Ann. Intern. Med. 175, ITC33–ITC48 (2022). - PubMed - DOI
• Ebekozien, O. et al. Longitudinal trends in glycemic outcomes and technology use for over 48,000 people with type 1 diabetes (2016-2022) from the T1D exchange quality improvement collaborative. Diabetes Technol. Ther. 25, 765–773 (2023). - PubMed - DOI

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A new therapy for treating Type 1 diabetes

Promising early results show that longstanding harvard stem cell institute (hsci) research may have paved the way for a breakthrough treatment of type 1 diabetes. utilizing research from the melton lab, vertex pharmaceuticals has developed vx-880, an investigational stem cell-derived, fully differentiated pancreatic islet cell replacement therapy for people with type 1 diabetes (t1d). in conjunction with immunosuppressive therapy, vx-880 produced robust restoration of islet cell function on day 90 in the first patient in its phase 1/2 clinical trial..

The patient was treated with a single infusion of VX-880 at half the target dose in conjunction with immunosuppressive therapy. The patient, who was diagnosed with T1D 40 years ago and has been dependent on exogenous (injected) insulin, achieved successful engraftment and demonstrated rapid and robust improvements in multiple measures. These included increases in fasting and stimulated C-peptide, improvements in glycemic control, including HbA1c, and decreases in exogenous insulin requirement, signifying the restoration of insulin-producing islet cells.

VX-880 is not only a potential breakthrough in the treatment of T1D, it is also one of the very first demonstrations of the practical application of embryonic stem cells, using stem cells that have been differentiated into functional islets to treat a patient, explained Doug Melton, Ph.D., co-director of HSCI, is the Xander University Professor at Harvard and an Investigator of the Howard Hughes Medical Institute. Unlike prior treatments, this innovative therapy gives the patient functional hormone producing cells that control glucose metabolism. This potentially obviates the lifelong need for patients with diabetes to self-inject insulin as the replacement cells “provide the patient with the natural factory to make their own insulin,” explained Melton.

These results from the first patient treated with VX-880 are unprecedented. What makes these results truly remarkable is that they were achieved with only half the target dose,” said Bastiano Sanna, Ph.D., Executive Vice President and Chief of Cell and Genetic Therapies at Vertex. “While still early, these results support the continued progression of our VX-880 clinical studies, as well as future studies using our encapsulated islet cells, which hold the potential to be used without the need for immunosuppression.”

“As a surgeon who has worked in the field of islet cell transplantation for decades, this approach, which obviates the need for an organ donor, could be a game changer,” said James Markmann, M.D., Ph.D., Professor of Surgery and Chief of the Division of Transplant Surgery at Massachusetts General Hospital. “We are excited to progress this unique and potentially transformative medicine through clinical trials and to patients.”

“More than a decade ago our lab had a vision for developing an islet cell replacement therapy to provide a functional cure to people suffering from T1D,” said Melton, a founder and one of the first co-chairs of the Harvard Stem Cell and Regenerative Biology Department. “These promising results bring great hope that stem cell-derived, fully differentiated islet cells could deliver a life-changing therapy for people who suffer from the relentless life-long burden of T1D. I'm so grateful that Harvard and the Harvard Stem Cell Institute have supported this work.”

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• Published: 02 September 2024

Perceptions of adults with type 1 diabetes toward diabetes-specific quality of life measures: a survey-based qualitative exploration

• Elizabeth Holmes-Truscott 1 , 2 , 3 ,
• Jasmine Schipp 1 , 2 , 3 , 4 ,
• Debbie D. Cooke 5 , 6 ,
• Christel Hendrieckx 1 , 2 , 3 ,
• Elizabeth J. Coates 7 ,
• Simon R. Heller 8 &
• Jane Speight 1 , 2 , 3  

Health and Quality of Life Outcomes volume  22 , Article number:  70 ( 2024 ) Cite this article

Metrics details

Diabetes-specific quality of life (QoL) questionnaires are commonly used to assess the impact of diabetes and its management on an individual’s quality of life. While several valid and reliable measures of diabetes-specific QoL exist, there is no consensus on which to use and in what setting. Furthermore, there is limited evidence of their acceptability to people with diabetes. Our aim was to explore perceptions of adults with type 1 diabetes (T1D) toward five diabetes-specific QoL measures.

Adults (aged 18 + years) with T1D living in Australia or the United Kingdom (UK) were eligible to take part in ‘YourSAY: QoL’, an online cross-sectional survey. Recruitment involved study promotion on diabetes-related websites and social media, as well as direct invitation of people with T1D via a hospital client list (UK only). In random order, participants completed five diabetes-specific QoL measures: Audit of Diabetes-Dependent Quality of Life (ADDQoL-19); Diabetes Care Profile: Social and Personal Factors subscale (DCP); DAWN Impact of Diabetes Profile (DIDP); Diabetes-Specific Quality of Life Scale: Burden Subscale (DSQoLS); Diabetes Quality of Life Questionnaire (Diabetes QOL-Q). They were invited to provide feedback on each questionnaire in the form of a brief free-text response. Responses were analysed using inductive, thematic template analysis.

Of the N  = 1,946 adults with T1D who completed the survey, 20% (UK: n  = 216, Australia: n  = 168) provided qualitative responses about ≥ 1 measure. All measures received both positive and negative feedback, across four themes: (1) clarity and ease of completion, e.g., difficulty isolating impact of diabetes, dislike of hypothetical questions, and preference for ‘not applicable’ response options; (2) relevance and comprehensiveness, e.g., inclusion of a wide range of aspects of life to improve personal relevance; (3) length and repetition, e.g., length to be balanced against respondent burden; (4) framing and tone, e.g., preference for respectful language and avoidance of extremes.

Conclusions

These findings suggest opportunities to improve the relevance and acceptability of existing diabetes-specific QoL measures, and offer considerations for developing new measures, which need to be better informed by the preferences of people living with diabetes.

Diabetes-specific quality of life (QoL) refers to an individual’s perception of the impact of diabetes on their QoL, or how diabetes affects aspects of life important to them [ 1 , 2 ]. Several diabetes-specific QoL assessment tools exist [ 2 , 3 , 4 , 5 ], with no consensus on which to use and in what setting. Likely relatedly, there has also been a lack of systematic QoL assessment (diabetes-specific or generic) in diabetes research and clinical practice [ 1 ]. In response, practical guidance and frameworks have been proposed to support appropriate diabetes-specific QoL questionnaire selection [ 1 , 3 , 6 ], including consideration of questionnaire aims, intended population, rigour of development process, psychometric properties, sensitivity to change, participant burden, content face validity, and acceptability among the intended audience. While considerable evidence for the psychometric properties of established diabetes-specific QoL questionnaires (across populations and linguistic translations) exists, responder perceptions toward, and acceptability of, questionnaire’s remains limited [ 3 , 4 , 7 ].

Involvement of the intended group in questionnaire design and refinement is considered a methodological necessity to assure content validity and acceptability [ 8 , 9 , 10 , 11 ]. However, the extent to which people with diabetes have traditionally been consulted in the development of diabetes-specific QoL measures varies (see Table  1 ), and few subsequent studies have examined respondent perceptions to inform questionnaire refinement or selection [ 7 , 12 , 13 ]. While some more recently developed questionnaires have involved such community involvement and piloting [ 14 ], questionnaires developed decades ago continue to be used most widely and whether they remain fit for purpose or are acceptable to contemporary study participants warrants consideration. Researchers and clinicians must rely on the typically sparse details reported within in scale development and psychometric testing publications, with questionnaire acceptability often indicated by response rate rather the respondent perceptions.

Previously, we reported quantitative findings of the ‘Your Self-management And You: Quality of Life’ (YourSAY: QoL) study, which was the first ‘head-to-head’ comparison of the psychometric properties and acceptability of five questionnaires designed to assess diabetes-specific QoL among adults with type 1 diabetes (T1D) living in Australia and the United Kingdom (UK) [ 7 ]. The findings suggested largely positive and consistent acceptability user ratings across the diabetes-specific QoL measures, as quantitatively derived by five study-specific single-items on a 5-point Likert scale. However, these published data provide no insights into the specific reasons for the largely positive views, nor explanation for respondents’ negative ratings. In addition to the user ratings, survey participants were invited to provide brief qualitative feedback. In combination with published development processes and psychometric evaluation, the preferences of people with T1D can inform recommendations for the selection of diabetes-specific QoL tools as well as the improvement of existing and novel questionnaires can be made.

Therefore, the current study explores the qualitative feedback collected in the YourSAY: QoL survey to understand what adults with T1D in Australia and the UK like and dislike about five contemporary and/or commonly used diabetes-specific QoL measures.

The YourSAY: QoL study was a cross-sectional survey administered online, using a pragmatic mixed-methods approach to explore questionnaire acceptability. Study methods have been described in detail elsewhere, and are included below [ 7 ]. For this substudy, a descriptive theoretical framework was employed with the aim of providing a comprehensive summary of participant questionnaire perceptions and preferences. This approach is well-suited to ‘thin’ data collected via survey free-text responses.

Participants and recruitment

Eligible participants for the overall survey were adults (aged 18 + years) with a self-reported diagnosis of T1D or type 2 diabetes (T2D), living in either Australia or the UK. Participants were recruited using convenience sampling through websites, e-newsletters/blogs and social media (Twitter, Facebook). In the UK only, a social media advertising company was contracted to promote study advertisements using Facebook, and 1,921 consenting adults with T1D under the care of Sheffield Teaching Hospitals NHS Trust were invited to take part (via letter or email). Potential participants were directed to an online survey hosted via Qualtrics™ (a secure online survey platform). Following informed consent and eligibility screening, eligible participants were directed to the survey proper.

The YourSAY: QoL survey was completed by N  = 4166 participants (T1D: n  = 1946, 47%). Inclusion criteria for the current analysis were self-reported T1D, and provision of qualitative feedback on at least one attempted diabetes-specific QoL questionnaires. Participant flow and reasons for exclusion are detailed in Fig.  1 . The current qualitative study reports on a subsample of n  = 384 YourSAY: QoL participants.

Participant flow and reasons for exclusion

Participants were invited to complete five diabetes-specific QoL measures (detailed in Table  1 ).The questionnaire selection process and psychometric analyses are reported elsewhere [ 7 ]. The five Diabetes-specific QoL measures were presented in random order to control for order effects, and optimise complete data per questionnaire in the case of early drop off. Following each questionnaire, participants were presented with five study-specific questions in which they were asked to rate ( 5-point scale: 1 = strongly disagree to 5 = strongly agree), the clarity, relevance, ease of completion, length, and comprehensiveness of the questionnaire (reported elsewhere) [ 7 ]. Participants were then asked the following free-text qualitative question: “Your feedback is important to us. Please feel free to comment below on anything you particularly liked or disliked about this questionnaire”.

Demographic (age, sex, location of residence, primary language) and self-reported clinical data (diabetes duration, primary treatment, number of diabetes-related complications) were also collected.

Data handling and analysis

Descriptive statistics for key demographic and clinical characteristics and differences between ‘responders’ (eligible final sample) and non-responders (participants with T1D who attempted ≥ 1 measure but provided no qualitative feedback) were calculated (IBM SPSS Statistics). Between group differences were assessed via Student’s t-tests for continuous variables and Pearson’s Chi-Square for categorical data.

Qualitative data were screened for invalid responses (e.g. “N/A”, “nil”) and uploaded to QSR NVivo for thematic template analysis, applying an inductive (data-driven) approach [ 22 ]. In template analysis, a coding template (which summarises the (sub)themes in a meaningful order) is developed, refined and applied iteratively to the data. This approach is well-suited to vast but shallow survey data collection, as template analysis does not require distinction between descriptive and interpretive themes. Following familiarisation with the data, J.Sc proposed an initial coding template, which was iteratively reviewed and refined following coding application by JSc and E.H-T, as well as discussion with J.Sp. Minimal coding disagreement was identified, discussed and resolved between authors. The final framework was applied to the remaining responses by J.Sc. Names and descriptions of the final themes and subthemes were agreed among all authors, and example quotes reviewed to ensure they represented the data adequately and reflected the study aims.

J.Sc, an undergraduate researcher at the time of analysis, led qualitative data analysis, reflecting on data initially without pre-conceived assumptions about measures, and discussing findings with the remaining authors (expertise: health psychology, health services, clinical diabetes) who drew on their deep understanding [ 1 , 2 ], and prior application of the assessed tools (including contributions to their development [ 21 ], refinement, [ 18 ] or English translation [ 20 ]) in their interpretation of findings. Representation of both positive and negative feedback for each measure was prioritised to challenge any predefined ideas about preferred measures.

Characteristics of the eligible sample (respondents, n  = 384), and those who attempted the diabetes-specific QoL questionnaires but did not provide qualitative feedback (non-respondents, n  = 1194), are shown in Table  2 .

Most respondents attempted all five diabetes-specific QoL measures of interest ( n  = 344, 90%) and provided qualitative feedback on one questionnaire ( n  = 220, 57%), most commonly for the DSQoLS (see Table  3 ). Across the five measures, a total of 711 qualitative responses were provided. A minority of participant feedback included general comments (e.g. all five questionnaires were perceived by some as “good”, “fine”, “liked”, or described as “enjoyable” and “interesting”) with no further detail. Specific feedback was organised within four main themes: (1) clarity and ease of completion; (2) relevance and comprehensiveness; (3) length and repetition, and (4) preferences and impact of questionnaire wording and tone. Overall themes and sub-themes are described below, and quotes relevant to the five questionnaires are shown in Table  3 .

Questionnaire clarity and ease of completion

In addition to general endorsements that questionnaires are “simple”, “easy”, and “straightforward”, participants provided specific feedback relating to the clarity of instructions and questionnaire wording; suitability of response options, and difficulty isolating the impact of diabetes on QoL.

Instructions and question wording

Differing views were offered about whether questionnaire instructions or questions were clear and easy to understand, as well as preferred questionnaire attributes. For instance, some participants praised the inclusion of examples in the Diabetes QOL-Q (‘I can go out or socialise as I would like, e.g., cinema, concerts…’), suggesting that it helped to clarify what is being asked, while another participant reported the inclusion of such examples as “patronising”.

Consistently, respondents indicated that the if/then wording used in the ADDQoL-19 was “confusing” and questions containing double negatives (i.e. item 17: “If I did not have diabetes, I would have to depend on others when I do not want to”) were “illogical” and “incomprehensible”. They also indicated that the broad question wording that encompassed diverse situations made it difficult to answer the questions. For example, item 3 of the DIDP incorporates three separate concepts (i.e. ‘relationship with your family, friends and peers’) and item 8 of the ADDQoL-19 combines current experience of or wish for a ‘close personal relationship’ within a single question. Participants also found overly-specific wording did not capture their actual experience. For example, two DCP items assess the impact of diabetes on food intake, but not “when I would like” to eat. One respondent provided feedback on the seventh item of the modified DIDP (‘Your freedom to eat as you wish’), reporting that this question stood out and was “much more specific than the other[s]”.

For the DCP and DSQoLS, participants also indicated that the timeframe referred to in the instructions (e.g. “in the past 4 weeks”, “in the past year”) was confusing, “easily forgotten”, or “too long”.

Response options

There was mixed feedback regarding the preferred number of response options and phrasing. Some participants reported liking the bi-directional response scale used in the DIDP, while others stated that they found it confusing or that it did not account for the potential combined positive and negative impacts of diabetes on particular aspects of life, such as physical health. In general, participants favoured inclusion of a “not applicable” response option, and some reported that this option was missing from the DCP and DSQoLS. Across measures, several participants indicated a desire to explain the underlying reasons for their response and participants positively reviewed the inclusion of an open-ended question inviting a free-text response in the ADDQoL-19.

Difficulty assessing the impact of diabetes

In rating the impact of diabetes on a particular aspect of life, the ADDQoL-19 asks participants to imagine their life without diabetes (e.g. ‘If I did not have diabetes, my quality of life would be…’). Though some participants reported that the opportunity to reflect on their life without diabetes was “an interesting concept”, others disliked this hypothetical phrasing and found the questions “impossible” to answer. While the other four measures of interest do not instruct participants to compare or rate their QoL against a life without diabetes, some participants reported difficultly responding as it’s “the only life I know” and that they may have over- or underestimated the impact of diabetes on their QoL. Participants also reported difficulty isolating diabetes from the impact of other life factors (e.g., other health condition, responsibilities, financial challenges). Some indicated a preference for a generic measure, which would allow participants to rate their QoL overall.

Questionnaire comprehensiveness and relevance

Where participants reported questionnaires as irrelevant, some indicated an inability to identify with the questionnaire, or that the questionnaire assumed a view of diabetes that did not reflect (their) reality. The lengthy DSQoLS (57 items) had the most references discussing perceptions of its (ir)relevance, while feedback on the shorter DIDP (7 items) indicated that it was “simplistic”.

Relevance of specific aspects of life and perceived omissions

Conflicting feedback was identified regarding the relevance of measured domains, particularly related to the impact of diabetes on finances. More typically, UK participants perceived such questions as irrelevant and suggested their removal, while Australian respondents endorsed such questions as relevant. Other specific questionnaire items reported as relevant included future health and development of diabetes complications (DSQoLS); emotional well-being (DIDP and DSQoLS, ); body image and family relationships (Diabetes QOL-Q). In contrast, irrelevant items commonly related to romantic partners and intimacy (DSQoLS, ADDQoL-19); driving (Diabetes QOL-Q); eating as you wish (ADDQoL-19); treatment modality-specific questions (DSQoLS), and experience of hypoglycaemia (DSQoLS).

Table  4 lists topics, or aspects of life, which participants perceived as missing from a questionnaire or not adequately assessed. Across questionnaires, commonly perceived omissions related to the impact of diabetes on mental / emotional health, and on finances, as well as the impact of diabetes management activities (e.g. insulin administration modality; diet; glucose monitoring) and extreme glucose levels on overall QoL. The Diabetes QOL-Q had the fewest references to omissions overall ( n  = 22), while the DSQoLS had the most reported omissions ( n  = 69), of which most referred to the impact of hyperglycaemia.

Questionnaire length and repetition

Related to, but discrete from comprehensiveness, participants commented on the length of measures, and were divided in their preference for brevity versus breadth. For example, the 7-item DIDP was described both as “brief and direct” and “so brief it was almost offensive”. Similarly, the 57-item DSQoLS was reported by some to be “too long” and by others as allowing for a detailed or comprehensive assessment (Theme 2). Item repetition was reported for the DSQoLS, to a lesser degree for the DCP and Diabetes QOL-Q and not at all for the ADDQoL-19 or the DIDP. Participants reported that repetitive questioning was “depressing”, overstated the relevance of certain topics/life aspects (i.e. the 11 DSQoLS items examining the impact of hypoglycaemia), and left them feeling more “worried”.

Questionnaire framing

Some respondents reported disliking the negative framing used by the ADDQoL-19, DCP and DSQoLS because they perceived it as placing a focus on the limitations of diabetes. In contrast, others appreciated the ADDQoL-19’s recognition of the “negative aspects of life”. Feedback about the positively-framed Diabetes QOL-Q was similarly divided between those who liked the “more positive manner” and those who felt it painted a “falsely positive picture”. Participants also reflected on the specific words and phrases used, reporting that certain terms (e.g. ‘normal’ and ‘control’ in the Diabetes QOoL-Q; ‘burden’, ‘worry’, ‘bother’ in the DSQoLS) made them feel like a “victim” or implied that diabetes controlled them. In contrast, the language used in the ADDQoL-19 was described as “extremely respectful”.

Consistent with quantitative user rating assessment [ 7 ], study findings suggest that there is no unequivocally favoured diabetes-specific QoL measure among adults with T1D from Australia and the UK. However, an acceptable measure needs to be easy to understand and complete; comprehensive and personally relevant; brief, without repetition; neither overly negative/nor positive; and adopting respectful language. Review of the questionnaires’ attributes (Table  1 ) suggests greatest alignment to reported preferences for the ADDQoL-19, DIDP, and Diabetes QOoL-Q (i.e. fewer reported omissions, greater perceived relevance, opportunity for personalisation, no/low repetition of domains, and/or neutrally worded). These three measures were also previously identified as having strongest psychometric performance among YourSAY: QOL participants with T1D [ 7 ]. Consideration and application of respondents’ preferences in the review of existing measures, or the development of new questionnaires, may further improve acceptability, respondent experience and data quality for future studies.

Careful consideration of questionnaire wording is needed to minimise cognitive load and improve acceptability [ 23 ]. For example, the ADDQoL-19 has been criticized for its unique use of hypothetical questioning [ 2 , 24 ], which is presumed to be cognitively demanding [ 24 , 25 ], and is not recommended [ 9 ]. While ADDQoL-19 developers suggest that such questioning results in a more realistic assessment of the impact of diabetes [ 26 ], the differential basis for hypothetical responses (e.g. some may recall a time pre-diabetes, others may draw on social comparisons) may impact data reliability. Interestingly, difficulty assessing the specific impact of diabetes due to a lack of comparison (i.e. life without diabetes) or inability to isolate its impacts (i.e. from other health conditions or life factors) was not unique to the ADDQoL-19, but reported across all measures. Relatedly, a preference for a more holistic measure of health-related or general QoL (e.g. the new EQ-HWB) [ 27 ] was reported by some participants, while for others, diabetes-specific questionnaires enabled a unique opportunity for reflection and acknowledgment of the challenges of diabetes. Further qualitative research might examine individual differences in how respondents interpret and complete condition-specific questionnaires.

Some participants reported difficultly completing questions that included double-barreled concepts (e.g. DIDP item: ‘your relationship with your family, friends and peers’) or were too broad (e.g. DIDP item: ‘your physical health’). Their separation into distinct items may be considered, though this has implications for scale brevity and could place too much emphasis on specific domains (as was reported for the DSQoLS regarding hypoglycaemia and emotional burden). Regardless, omission of important QoL domains was not more commonly reported for the brief DIDP (which higher-order type wording) and good concurrent validity between DIDP total scores and longer measures has been established [ 7 ]. Thus, the DIDP may be appropriate where the intention is to measure the overall impact of diabetes on QoL, and/or identify global domains for further assessment or clinical discussion. Regardless of item specificity, or consistency with theorised domains of diabetes-specific QoL [ 28 ], none of the examined measures in the current study were without perceived omissions of important life aspects (domains).

Determinants of QoL are subjective and, accordingly, it is argued that QoL assessment should be tailored to the aspects of life deemed most important to a given individual [ 29 ]. For example, several participants commented on the irrelevance of finances, while others expressly reported the importance or omission of this domain. The inclusion and rating of irrelevant or unimportant QoL domains, and the exclusion of other relevant domains, may reduce meaningful scoring and/or lead to misguided intervention. However, few QoL questionnaires allow for such personalised assessment [ 30 ]. In an effort to personalise QoL within a standardised approach, the ADDQoL-19 employs average weighted impact scores incorporating perceived domain importance within scoring, and three measures allow for non-applicable responses to all (DIDP, Diabetes QOL-Q) or certain (ADDQoL-19) domains, which was viewed favourably by participants. Though applicability is not synonymous with importance. The collection of qualitative data in companion with quantitatively assessed QoL might help bridge the divide between personalised and standardized assessment [ 31 ]. Participants reported appreciation of the ADDQoL-19 free-text question, and across other measures reported a desire to elaborate on their responses. Regardless of the measure selected, inclusion of a free-text question might be considered for the mutual benefit of examining survey acceptability, increasing insights into the experience of diabetes, and/or identifying areas for clinical discussion. It is important that ethical consideration is given to the intended use of such data, and its collection is justifiable.

Interestingly, negative feedback suggesting scale irrelevance, and omission of important life aspects more typically related to the considerably longer DSQoLS, which does not allow for ‘not applicable’ responses and is the only evaluated measure intended only for use among adults with T1D (i.e. not also for use in T2D). At previously noted [ 7 ], the DSQOLS item profile is dissimilar to other questionnaires assessed, with inclusion of items relating to diabetes management, symptoms, fear of hypoglycaemia, and emotional burden. In contrast, the ADDQoL-19, DIDP and Diabetes QOL-Q assess the impact of diabetes on more global aspects of life, which may be consistent across diabetes types, treatments and experiences. However, the current findings cannot speak to the perceptions of, nor preferred measure attributes among those with T2D. It is possible that planned future inspection of the acceptability and psychometric performance of relevant questionnaires among participants with T2D may highlight differential preferences and scale performance, suggesting the need for tailored questionnaire selection, revision, or development by diabetes type (as has been a recent focus in the assessment of diabetes distress [ 32 , 33 ]).

The overall strengths and limitations of the YourSAY: QoL study are detailed elsewhere [ 7 ]. A key strength of the current qualitative study is the survey method, which permitted feedback from a large sample, extending on prior quantitative exploration of questionnaire acceptability [ 7 ] and qualitative methods used to inform questionnaire development and/or debriefing. However, the inability to follow up participants and invite additional information is a limitation of the current approach. Further, a minority of participants provided qualitative feedback (likely due in part to the burdensome overall survey length which resulted in substantial participant attrition) and this group were found to be more highly educated and engaged and slightly less negatively impacted by diabetes, compared to the broader sample. Thus, this study does not address the prior gaps of biased samples when developing or validating measures, and further acceptability assessment is warranted to explore questionnaire perceptions among diverse subgroups (e.g. those with ethnically diverse background, low English proficiency and/or (health) literacy). Future questionnaire adaptations and design should be informed by, and tested within, the intended population prior to use. Finally, other potentially relevant measures have been excluded from the current study, such as those published in a language other than English and/or novel questionnaires developed and validated since conducting the study. Future research might employ the methods designed here or draw on the identified subthemes to evaluate the acceptability of other measures and / or within populations not incorporated in the current study.

This novel large-scale qualitative study identified user perceptions, and preferred measurement attributes, of five diabetes-specific QoL measures among adults with T1D. Study findings complement our previous psychometric ‘head-to-head’ comparison [ 7 ], and identified the ADDQoL-19, DIDP and Diabetes QOL-Q as more typically incorporating preferred questionnaire attributes. These data, in combination with published development processes and psychometric evaluation, may be used to inform future questionnaire selection as well as best-placed resourcing for the improvement of existing and development novel diabetes-specific QoL measures. For example, findings from the current study directly informed assessment of diabetes-specific QoL within the DAFNE plus (Dose Adjustment For Normal Eating) cluster randomised controlled trial [ 34 ]. Specifically, the ADDQoL was selected as the primary psychosocial outcome measure, reflecting scale acceptability, psychometric performance, as well as existing evidence for scale responsiveness to intervention (including DAFNE [ 35 ]). The DIDP was also included among DAFNE plus assessment tools, with planned assessment of predictive validity and responsiveness to examine appropriateness of this much briefer tool for use in future interventional studies.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Audit of Diabetes-Dependent Quality of Life (ADDQoL-19)

Diabetes Care Profile

DAWN Impact of Diabetes Profile

Diabetes-Specific Quality of Life Scale

Diabetes Quality of Life Questionnaire

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Acknowledgements

We thank the adults with type 1 diabetes who participated.

This paper summarises independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme, as part of the study ‘Developing and trialling the DAFNE plus (Dose Adjustment for Normal Eating) intervention. A lifelong approach to promote effective self-management in adults with type 1 diabetes’ (Grant Reference Number RP-PG-0514-20013). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. E.H-T, C.H and J.Sp are/were supported by the core funding to the Australian Centre for Behavioural Research in Diabetes provided by the collaboration between Diabetes Victoria and Deakin University.

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J.Sp. and D.C. conceived the study and designed it with E.H-T., E.C. and S.H. E.H-T developed the survey and managed data collection. J.Sc. and E.H-T conducted data cleaning and analysis. All authors provided input into interpretation of results. E.H-T. and J.Sc. prepared the first draft of the manuscript. All authors contributed to manuscript revisions and approved the final version.

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Ethical approval was received in Australia (Deakin University Human Research Ethics Committee: HEAG-H 03_2017) in accordance with the Australian ‘National Statement on Ethical Conduct in Human Research 2023’. Ethics approval was also received in the UK (Yorkshire & The Humber-Bradford Leeds Research Ethics Committee: 17/YH/0234; Health Research Authority: IRAS ID: 228898). Participants accessed a plain language statement and provided informed consent prior to completing the survey.

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This article includes data presented at a scientific meeting: Schipp J, Holmes-Truscott E, Hendrieckx C, Coates E, Heller S, Cooke D, Speight J. A qualitative investigation of the acceptability to adults with type 1 diabetes of five diabetes-specific quality of life measures. Findings of the YourSAY: Quality of Life study. Australasian Diabetes Congress (ADC) 2019, Sydney, Australia, 21-23 August 2019

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Holmes-Truscott, E., Schipp, J., Cooke, D.D. et al. Perceptions of adults with type 1 diabetes toward diabetes-specific quality of life measures: a survey-based qualitative exploration. Health Qual Life Outcomes 22 , 70 (2024). https://doi.org/10.1186/s12955-024-02285-4

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Health Literacy Among Young People With Type 1 Diabetes: A Qualitative Study of Patient Involvement

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Margit Oien Nielsen , Hanne Frejlev , Anette Vestermark , Liva B. Larsen , Anna Pietraszek , Jakob Dal , Dorte Melgaard; Health Literacy Among Young People With Type 1 Diabetes: A Qualitative Study of Patient Involvement. Diabetes Spectr 2024; ds240006. https://doi.org/10.2337/ds24-0006

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This qualitative study explored the challenges and knowledge gaps among Danish youth with type 1 diabetes and subsequently introduced an information program to empower these youth in their diabetes self-management.

Nine young patients 18–25 years of age who were diagnosed with type 1 diabetes, living independently, or cohabiting with a partner, were included. Relevant participants were invited by mail or telephone. Group interviews were conducted to uncover the specific knowledge requirements essential for improving their diabetes health literacy. Based on these interviews, four education sessions were held.

The participants identified pertinent topics, including alcohol consumption, blood glucose control, educational and employment aspects, nutrition, sexuality, pregnancy, relationships, and interactions with health care professionals (HCPs). Notably, the participants expressed a preference for personalized interactions over information dissemination through digital platforms such as mobile applications. Building on this insight, we organized four sessions to provide education on the identified subjects. These sessions were designed to facilitate networking among participants and offer them the opportunity for discussion. Although invitations were extended to all individuals aged 18–25 with type 1 diabetes ( n = 52), only 13 patients and seven relatives participated. The feedback from attendees was overwhelmingly positive. Reasons for nonparticipation included forgetfulness or a reluctance to engage in group settings.

Young people with type 1 diabetes appreciate personal contact with HCPs. They do not want to receive knowledge via digital apps and virtual media but instead to meet with equals. However, it remains difficult to involve them in social events. The problem of how to create contact with young people with type 1 diabetes to strengthen their health literacy remains unsolved, and there is a need for further innovative initiatives.

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Research Gaps Around Type 1 Diabetes

A large body of research on Type 2 diabetes has helped to develop guidance, informing how patients are diagnosed, treated, and manage their lifestyle. In contrast, Type 1 diabetes, often mistakenly associated only with childhood, has received less attention.

In this Q&A, adapted from the  April 17 episode of Public Health On Call , Stephanie Desmon speaks to Johns Hopkins epidemiologists  Elizabeth Selvin , PhD '04, MPH, and  Michael Fang , PhD, professor and assistant professor, respectively, in the Department of Epidemiology, about recent findings that challenge common beliefs about type 1 diabetes. Their conversation touches on the misconception that it’s solely a childhood condition, the rise of adult-onset cases linked to obesity, and the necessity for tailored approaches to diagnosis and care. They also discuss insulin prices and why further research is needed on medications like Ozempic in treating Type 1 diabetes.

I want to hear about some of your research that challenges what we have long understood about Type 1 diabetes, which is no longer called childhood diabetes. 

MF: Type 1 diabetes was called juvenile diabetes for the longest time, and it was thought to be a disease that had a childhood onset. When diabetes occurred in adulthood it would be type 2 diabetes. But it turns out that approximately half of the cases of Type 1 diabetes may occur during adulthood right past the age of 20 or past the age of 30.

The limitations of these initial studies are that they've been in small clinics or one health system. So, it's unclear whether it's just that particular clinic or whether it applies to the general population more broadly. 

We were fortunate because the CDC has collected new data that explores Type 1 diabetes in the U.S. Some of the questions they included in their national data were, “Do you have diabetes? If you do, do you have Type 1 or Type 2? And, at what age were you diagnosed?”

With these pieces of information, we were able to characterize how the age of diagnosis of Type 1 diabetes differs in the entire U.S. population.

Are Type 1 and Type 2 diabetes different diseases?

ES:  They are very different diseases and have a very different burden. My whole career I have been a Type 2 diabetes epidemiologist, and I’ve been very excited to expand work with Type 1 diabetes.

There are about 1.5 million adults with Type 1 diabetes in the U.S., compared to 21 million adults with Type 2 diabetes. In terms of the total cases of diabetes, only 5 to 10 percent have Type 1 diabetes. Even in our largest epidemiologic cohorts, only a small percentage of people have Type 1 diabetes. So, we just don't have the same national data, the same epidemiologic evidence for Type 1 diabetes that we have for Type 2. The focus of our research has been trying to understand and characterize the general epidemiology and the population burden of Type 1 diabetes.

What is it about Type 1 that makes it so hard to diagnose?

MF: The presentation of symptoms varies by age of diagnosis. When it occurs in children, it tends to have a very acute presentation and the diagnosis is easier to make. When it happens in adulthood, the symptoms are often milder and it’s often misconstrued as Type 2 diabetes. 

Some studies have suggested that when Type 1 diabetes occurs in adulthood, about 40% of those cases are misdiagnosed initially as Type 2 cases. Understanding how often people get diagnosed later in life is important to correctly diagnose and treat patients. 

Can you talk about the different treatments?

MF:  Patients with Type 1 diabetes are going to require insulin. Type 2 diabetes patients can require insulin, but that often occurs later in the disease, as oral medications become less and less effective.

ES: Because of the epidemic of overweight and obese in the general population, we’re seeing a lot of people with Type 1 diabetes who are overweight and have obesity. This can contribute to issues around misdiagnosis because people with Type 1 diabetes will have signs and will present similarly to Type 2 diabetes. They'll have insulin resistance potentially as a result of weight gain metabolic syndrome. Some people call it double diabetes—I don't like that term—but it’s this idea that if you have Type 1 diabetes, you can also have characteristics of Type 2 diabetes as well.

I understand that Type 1 used to be considered a thin person's disease, but that’s not the case anymore.  MF:  In a separate paper, we also explored the issue of overweight and obesity in persons with Type 1 diabetes. We found that approximately 62% of adults with Type 1 diabetes were either overweight or obese, which is comparable to the general U.S. population.

But an important disclaimer is that weight management in this population [with Type 1 diabetes] is very different. They can't just decide to go on a diet, start jogging, or engage in rigorous exercise. It can be a very, very dangerous thing to do.

Everybody's talking about Ozempic and Mounjaro—the GLP-1 drugs—for diabetes or people who are overweight to lose weight and to solve their diabetes. Where does that fit in with this population?

ES: These medications are used to treat Type 2 diabetes in the setting of obesity. Ozempic and Mounjaro are incretin hormones. They mediate satiation, reduce appetite, slow gastric emptying, and lower energy intake. They're really powerful drugs that may be helpful in Type 1 diabetes, but they're  not approved for the management of obesity and Type 1 diabetes. At the moment, there aren't data to help guide their use in people with Type 1 diabetes, but I suspect they're going to be increasingly used in people with Type 1 diabetes.

MF:   The other piece of managing weight—and it's thought to be foundational for Type 1 or Type 2—is dieting and exercising. However, there isn’t good guidance on how to do this in persons with Type 1 diabetes, whereas there are large and rigorous trials in Type 2 patients. We’re really just starting to figure out how to safely and effectively manage weight with lifestyle changes for Type 1 diabetics, and I think that's an important area of research that should continue moving forward.

ES: Weight management in Type 1 diabetes is complicated by insulin use and the risk of hypoglycemia, or your glucose going too low, which can be an acute complication of exercise. In people with Type 2 diabetes, we have a strong evidence base for what works. We know modest weight loss can help prevent the progression and development of Type 2 diabetes, as well as weight gain. In Type 1, we just don't have that evidence base.

Is there a concern about misdiagnosis and mistreatment? Is it possible to think a patient has Type 2 but they actually have Type 1? 

MF: I think so. Insulin is the overriding concern. In the obesity paper, we looked at the percentage of people who said their doctors recommended engaging in more exercise and dieting. We found that people with Type 1 diabetes were less likely to receive the same guidance from their doctor. I think providers may be hesitant to say, “Look, just go engage in an active lifestyle.”

This is why it's important to have those studies and have that guidance so that patients and providers can be comfortable in improving lifestyle management.

Where is this research going next?

ES:  What's clear from these studies is that the burden of overweight and obesity is substantial in people with Type 1 diabetes and it's not adequately managed. Going forward, I think we're going to need clinical trials, clear clinical guidelines, and patient education that addresses how best to tackle obesity in the setting of Type 1 diabetes.

It must be confusing for people with Type 1 diabetes who are   hearing about people losing all this weight on these drugs, but they go to their doctor who says, “Yeah, but that's not for you.”

ES: I hope it's being handled more sensitively. These drugs are being used by all sorts of people for whom they are not indicated, and I'm sure that people with Type 1 diabetes are accessing these drugs. I think the question is, are there real safety issues? We need thoughtful discussion about this and some real evidence to make sure that we're doing more good than harm.

MF:  Dr. Selvin’s group has published a paper, estimating that about 15% of people with Type 1 diabetes are on a GLP-1. But we don't have great data on what potentially can happen to individuals.

The other big part of diabetes that we hear a lot about is insulin and its price. Can you talk about your research on this topic?

MF:  There was a survey that asked, “Has there been a point during the year when you were not using insulin because you couldn’t afford it?” About 20% of adults under the age of 65 said that at some point during the year, they couldn't afford their insulin and that they did engage in what sometimes is called “cost-saving rationing” [of insulin].

Medicare is now covering cheaper insulin for those over 65, but there are a lot of people for whom affordability is an issue. Can you talk more about that? 

MF:  The fight is not over. Just because there are national and state policies, and now manufacturers have been implementing price caps, doesn't necessarily mean that the people who need insulin the most are now able to afford it. 

A recent study in the  Annals of Internal Medicine looked at states that adopted or implemented out-of-pocket cost caps for insulin versus those that didn't and how that affected insulin use over time. They found that people were paying less for insulin, but the use of insulin didn't change over time. The $35 cap is an improvement, but we need to do more.

ES: There are still a lot of formulations of insulin that are very expensive. $35 a month is not cheap for someone who is on insulin for the rest of their lives.

RELATED:  

• Overweight and Obesity in People With Type 1 Diabetes Nearly Same as General Population
• The Impacts of COVID-19 on Diabetes and Insulin
• Why Eli Lilly’s Insulin Price Cap Announcement Matters

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Review Series Free access | 10.1172/JCI142242

Type 1 diabetes mellitus: much progress, many opportunities

Alvin c. powers.

Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA. Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA.

Address correspondence to: Alvin C. Powers, Vanderbilt University Medical Center, 8435 MRBIV, 2215 Garland Ave, Nashville, Tennessee 37232, USA. Phone: 615.936.7678; Email: [email protected] .

Published March 24, 2021 - More info

As part of the centennial celebration of insulin’s discovery, this review summarizes the current understanding of the genetics, pathogenesis, treatment, and outcomes in type 1 diabetes (T1D). T1D results from an autoimmune response that leads to destruction of the β cells in the pancreatic islet and requires lifelong insulin therapy. While much has been learned about T1D, it is now clear that there is considerable heterogeneity in T1D with regard to genetics, pathology, response to immune-based therapies, clinical course, and susceptibility to diabetes-related complications. This Review highlights knowledge gaps and opportunities to improve the understanding of T1D pathogenesis and outlines emerging therapies to treat or prevent T1D and reduce the burden of T1D.

The discovery of insulin and its rapid incorporation into clinical practice is one the greatest examples of scientific research saving lives and transforming clinical care. Prior to the discovery of insulin, type 1 diabetes mellitus (T1D) was a uniformly lethal disease that led to either rapid death from diabetic ketoacidosis or, with adherence to a strict starvation protocol, severe malnutrition and death within months or 1–2 years. As outlined by historians ( 1 ), the discovery of insulin was a reprieve from the death sentence of T1D, such that in 1922–1923, the timing of T1D onset and access to insulin determined whether some individuals died or lived for many years ( 2 ). The stories of people rescued from certain death by insulin’s discovery are nothing short of miraculous. Multiple millions of individuals with T1D and their descendants are alive today because of insulin’s discovery.

This Review provides a brief, comprehensive overview of human T1D, including what is known about its genetics, pathogenesis, and natural history, before concluding with a discussion of current and future therapeutic or preventive strategies. This Review describes exciting advances in the understanding and treatment of human T1D, but also highlights gaps in our knowledge that must be overcome to reverse and/or prevent T1D and its related complications. A recurring theme will be the growing recognition that T1D is not a single, monolithic disease but is instead heterogenous, and that the term T1D likely encompasses several different pathological processes within the clinical phenotype of T1D. While this Review is aimed at medical and scientific readers, it is essential to recognize the critical roles played by individuals with T1D and their families in advancing the understanding and treatment of T1D. These individuals have participated in paradigm-changing clinical trials, advocated for biomedical research and new technologies, and incorporated many new approaches into their daily lives.

Traditionally, the diagnosis of T1D is based on the clinical phenotype of insulin-dependent diabetes with onset in childhood or adolescence and possibly diabetic ketoacidosis. With the increasing emphasis on precision medicine, most discussion of diabetes has focused on heterogeneity in type 2 diabetes (T2D) and distinguishing T2D from monogenic diabetes or atypical forms of diabetes ( 3 – 5 ), with the assumption that one can accurately define T1D based on the clinical phenotype. This view has been substantially altered by two major changes in our thinking: (a) T1D begins with evidence of islet-directed autoimmunity prior to appearance of dysglycemia or hyperglycemia; (b) T1D likely results from different pathways to β cell destruction as reflected by differences in age of onset, genetics, pancreas pathology, metabolism, insulin secretion, and ultimately by differences in response to therapies and diabetes-related complications.

The model proposed by George Eisenbarth in 1986 has long been the standard schematic for how T1D develops, and even with many noted caveats and inconsistencies, it remains useful in framing today’s discussion about the development of T1D ( Figure 1 and Table 1 ). Components, revisions, and limitations of this model are discussed below and include new information about genetic susceptibility, pathogenic and molecular aspects of β cell–directed autoimmunity, and the interactions between immunology and islet biology ( 6 ). The current model suggests that the natural progression of T1D in genetically susceptible individuals consists of three stages ( 7 ). In stage 1, normoglycemia is accompanied by two or more islet-directed autoantibodies. The autoimmune process, as reflected by these autoantibodies, is presumably initiated by an unidentified triggering event or events. In stage 2, the autoantibodies are accompanied by dysglycemia reflecting inadequate insulin secretion after a glucose or nutrient challenge. In stage 3, the time that T1D is usually clinically diagnosed, symptoms are usually present and insulin therapy is initiated. Presentation may range from diabetic ketoacidosis to modest hyperglycemia. β cells are still present and functional (it is estimated that ~60%–90% of β cell mass has been lost at clinical presentation; ref. 8 ). β Cell loss continues over the ensuing months to years, but many individuals with longstanding T1D still secrete small amounts of insulin. Although these stages are a helpful framework, it should be noted that not all individuals in stage 1 or stage 2 progress to hyperglycemia requiring insulin treatment. A significant limitation of the model is that it is almost entirely based on measurements gathered from the peripheral blood (insulin secretion, immunological markers, etc.) and clinical parameters (age of onset, BMI, exogenous insulin requirements, etc.); only recently have there been studies of the “scene of the crime,” the human pancreas, in stages 1, 2, and 3 and recent-onset T1D (discussed below).

Model of stages of type 1 diabetes (T1D). Graph shows functional β cell mass through the stages of T1D. The blue shaded area shows number or insulin secretory capacity of β cells, with time on the x axis reflecting a broad range (could be months or years of T1D development). See text for definition of T1D stages. Roman numerals on the graph refer to questions about T1D pathogenesis shown in Table 1 .

Questions about current model of T1D development

There is a lack of accepted, specific criteria for diagnosis of T1D. Instead, the clinical diagnosis continues to rely on two main features: (a) insulin deficiency and need for exogenous insulin therapy (insulin requirements may be modest in early stage 3 and the first years after hyperglycemia onset) and (b) presence of islet-directed autoantibodies. These criteria are reasonably accurate in individuals who develop diabetes prior to 20 years of age, but are considerably less informative in those over the age of 20 years, highlighting the need for additional criteria (e.g., genetic risk score, other immunological makers, subtypes of T1D). Other autoimmune endocrinopathies (autoimmune thyroid disease, autoimmune adrenal insufficiency, celiac disease, or atrophic gastritis) may be present in up to 20% of individuals with T1D and should be considered ( 9 ).

The precise number of individuals with T1D is not known, but more than 1.6 million people in the United States are thought to have T1D ( 10 ). The incidence of T1D varies widely among countries, but this likely reflects differences in T1D-susceptibility genetic loci in a country’s population rather than environmental exposures. T1D incidence is highest in Finland, Sardinia, Sweden, Norway, and Portugal, with the rate being 60- to 300-fold greater than in China and Venezuela ( 11 – 13 ). The incidence in the United States and Europe is intermediate. These rates of T1D are likely an underestimate and do not include many young adults and adults who develop T1D because almost all incidence data comes from registries with individuals under 20 years of age. The SEARCH for Diabetes in Youth study in the United States found a 1.4% per year increase in T1D incidence from 2002 to 2012, with an unexpected increase in Hispanic youths ( 14 ). This increase in the United States is similar to the gradual worldwide annual increase in T1D incidence over the past 30 years. The reasons for this increase are not known, but may reflect changing diet and environmental exposures. There are some signs, however, that this increase is not a continuing trend ( 13 ). The number of individuals developing T1D who have the high-risk HLA alleles is declining ( 15 ), suggesting that gene-environment interactions such as diet or environmental exposure play a role.

A genetic susceptibility to T1D, although incompletely defined, is clear: identical twins have a concordance rate of 60%–70% with long-term follow-up ( 16 ) and first-degree relatives have a 5%–6% lifetime risk of T1D. Despite the clear genetic risk, most individuals with T1D have no family history of T1D. Approximately 50% of the genetic risk is related to class II HLA alleles (DR, DQ, and others). Loci related to the insulin gene and variable number tandem repeat convey the next highest genetic risk ( 15 , 17 ). At least 50 other genetic loci or SNPs, mostly in regions of the genome predicted to be involved in gene regulation, have been identified as providing a small risk for T1D. Until recently, these loci were thought to affect immune cell function and influence nearby genes, but new data suggest that some directly affect the β cell, perhaps altering β cell response to inflammation or cytokines, such as IFN-γ or IL-1β, or act by influencing distant regulatory regions ( 17 ).

The polygenic nature of T1D genetic susceptibility has stimulated efforts to combine SNPs into a T1D genetic risk score (T1D-GRS). Such a T1D-GRS has proven useful when combined with autoantibody measurements in enhancing prediction of future T1D development in individuals at high risk for T1D who are being followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study ( 18 ). Similarly, application of such a T1D-GRS to the UK Biobank predicted that as many as 42% of individuals with T1D developed T1D over 30 years of age, highlighting the heterogeneity in T1D and the difficulty of diagnosing T1D in the adult population where T2D is much more frequent ( 19 ). For now, T1D-GRS is a research tool, but with further refinements it may prove useful in supporting or refuting the diagnosis of T1D in an individual or in population screening to identify individuals at high risk for developing T1D.

The pathological features of T1D, although often definitively stated (autoimmune, T cell–mediated β cell destruction accompanied by nonpathogenic, islet-directed autoantibodies), in reality has many unknowns, unproven assumptions, and knowledge gaps ( Figure 1 and Table 1 ). It is widely assumed that a triggering event or events initiate the β cell–directed autoimmunity, but these events or events remain unknown despite sustained efforts over many years. Many environmental agents have been postulated, including virus infection (Coxsackie, rubella, enterovirus, etc.), diet, intestinal microbiota, cleanliness of the environment, and gene-environmental interactions ( 20 – 24 ). The search continues in the TEDDY study, which is prospectively and systematically investigating potential environmental triggers.

Studies by a number of investigators have provided critical insight by collecting and studying the human pancreas from the few individuals who die at T1D onset, after a relatively short duration of T1D (<5 years), or who were found to have islet-related autoantibodies at the time of organ donation ( 25 – 35 ). Some of the pathological findings in the pancreas in recent-onset T1D include modest, patchy, variable insulitis (immune cell infiltration of islet); some pseudoatrophic, insulin-negative islets; and some islets with normal-appearing β cells ( Figure 2 ). The clinical features of T1D (age of diabetes diagnosis, BMI) and biomarkers (autoantibodies, C-peptide, T1D-GRS) mostly correlated with these pathological features of T1D ( 29 ), thus providing an important connection between peripheral measurements and pancreas pathology.

Pancreatic changes and immune abnormalities in T1D. The reduction of pancreas size or volume from normal to stage 2 to stage 3 T1D is shown at the top of the figure. The insets below shows a stylized section of the pancreas with islets and acinar cells in stage 2 and stage 3 T1D. Circulating autoantibodies directed at islet-enriched molecules are present in stage 2 and stage 3 but are not cytotoxic. In stage 3, immune cells are present within islets and exocrine pancreas, and there is a loss of both β cells and acinar cells. Other changes (not shown) in the stage 3 T1D islets include (a) insulin-negative, pseudoatrophic islets with rare islets appearing normal or having β cells; (b) alterations in proinsulin and insulin processing and expression of islet-enriched transcription factors such as PDX-1 and NKX6.1; (c) islet cell hyperexpression of HLA class I and class II molecules; (d) insulitis (immune cell infiltration in some islets) is variable, involving primarily CD8 + T cells, but also B lymphocytes, CD4 + T lymphocytes, and macrophages; CD20 + B lymphocytes are more common in recent-onset T1D in younger individuals; and (e) β cell mass is variable in stage 1, 2, and 3 (see Figure 1 ). Changes in the exocrine pancreas include (a) reduced pancreatic volume/mass at T1D onset and in autoantibody-positive individuals; progressive decline in pancreas volume in first 5 years of T1D; (b) acinar cell loss, some exocrine fibrosis in stage 3 pancreas; and (c) leukocyte infiltration of exocrine compartment. See text for additional details.

β Cell–directed autoimmunity involves humoral and cell-mediated autoimmunity ( Figure 2 ). Some combination of autoantibodies against glutamic acid decarboxylase (GAD), insulin (IAA), insulinoma-associated antigen-2 (IA-2), zinc transporter 8 (ZnT8), or tetraspanin-7 (Tspan7) are present at the onset of hyperglycemia in more than 90% of individuals with the typical T1D phenotype. These nonpathogenic autoantibodies are viewed as markers of the autoimmune process, with the presence of multiple autoantibodies during stage 2 in younger individuals predictive of transition to stage 3 over a 5- to 10-year timeline ( 36 ). In the TEDDY study that enrolled and followed from birth individuals genetically at high risk for T1D, insulin and GAD autoantibodies appeared in the first 5 years of life, sometimes within the first year of life, and the presence of 2 or more autoantibodies was associated with 60%–80% T1D development in follow-up over 10–15 years ( 37 , 38 ). The appearance of these autoantibodies is at a time of considerable change and plasticity in human islet morphology, cell composition, and maturation and immune system maturation and establishment, raising the possibility that developmental processes contribute to initiation of β cell–directed autoimmunity.

Despite the assumption that β cell destruction in T1D is mediated by T cells, it has been difficult to develop robust, standardized T cell–based assays ( 39 ). One reason is the low frequency of the diabetogenic T cells in the peripheral circulation of individuals with recent-onset T1D (stages 2 and 3) and the assumption that the pathogenic T cells of interest are within the islet or pancreas-draining lymph nodes. Recent work has validated this view with the isolation and cloning of CD4 + and CD8 + T cell lines from T1D islets and pancreatic lymph nodes that react to a range of islet antigens (including insulin, GAD, islet-specific glucose-6-phosphatase catalytic subunit–related protein, islet-associated amyloid polypeptide) and, interestingly, also to posttranslationally modified peptides or neoantigens (hybrid insulin peptides, etc.) ( 26 , 28 , 40 , 41 ). There is an increasing awareness that B lymphocytes are also involved in the autoimmune process ( 42 ).

β Cells exist within the human islet, a complex miniorgan ( 43 ), and according to current thinking, the β cell is not an innocent bystander simply targeted by a misdirected autoimmune process, but instead the β cell is an active participant either in the initiation or acceleration of the process leading to its own death ( 6 , 44 – 46 ). For example, the β cell’s extremely high rate of insulin biosynthesis and protein processing has been proposed to render it more susceptible to ER stress and the unfolded protein response, especially in the setting of cytokines released by infiltrating immune cells ( 47 ). Altered proinsulin processing in the pancreas and in the peripheral blood in T1D, including preclinical stages, has been noted and correlated with immune markers and markers of β cell stress ( 35 , 48 – 51 ). Multiplexed imaging mass cytometry studies of pancreatic samples collected by the Network for Pancreatic Organ donors with Diabetes ( 52 ) or the Human Pancreas Analysis Program ( 53 ) showed that at the time insulitis developed, some β cells had lost or changed expression of markers of β cell identity (C-peptide, PDX-1, NKX6.1), perhaps as a response or adaptation to immune attack ( 54 , 55 ). Perhaps portending the clinical heterogeneity of T1D, there was considerable variability in insulitis, β cell number, and gene expression within and between T1D pancreatic samples. Surprisingly, many islets in recent-onset T1D still had normal appearing β cells, consistent with observations that individuals with longstanding diabetes secrete small amounts of insulin and that the T1D pancreas continues to harbor insulin-positive cells that are glucose responsive ( 35 , 56 – 59 ). Although impaired glucagon and catecholamine secretion in response to hypoglycemia is a feature of T1D (especially longer duration T1D) and is thought to reflect autonomic neuropathy ( 60 ), recent information suggests that impaired α cell function with decreased expression of markers of cell identity (ARX) and ectopic expression of NKX6.1 in T1D islets also plays a role ( 55 , 59 ). It is not known whether the impaired α cell function is part of the T1D process or secondary to loss of β cell contact with α cells.

In T1D there is not only loss of β cells but the entire pancreas is smaller in individuals with longstanding T1D. More recently it has been found that pancreas size by MRI is reduced at the time of T1D onset and in stages 1, 2, and 3 ( 30 , 61 – 64 ). Since islets represent only 1%–2% of the pancreas, the exocrine compartment of the pancreas must also be affected in T1D ( Figure 2 ). Prior studies of pancreas size in T1D were conflicting, likely because they were cross-sectional autopsy studies or utilized a single noninvasive imaging session (ultrasound, computerized tomography, or MRI) that made it difficult to adequately account for the normal variation in pancreas size among individuals. Recent efforts, enabled by improvements in pancreas imaging and the availability of postmortem T1D human pancreata for study, have provided new information. For example, two MRI studies showed that pancreas volume is reduced at the time of clinical T1D onset, that it declines further in the first year after onset, and that it is reduced in autoantibody-positive individuals in stage 2 and in some first-degree T1D relatives ( 62 , 64 ). Pancreas size does not correlate with T1D duration, and it appears that the decline in pancreas size mostly occurs in the 5 years after clinical T1D onset ( 65 ).

The molecular mechanism responsible for the reduced volume is not clear, but there is reduced acinar cell number and evidence of fibrosis ( 61 , 65 ). Loss of an islet-derived trophic factor such as insulin has been proposed. T cell infiltration, including some T cells that recognize proinsulin, have been noted in the T1D exocrine pancreas ( 66 , 67 ). However, it is unclear how changes in the exocrine pancreas are temporally or mechanistically related to β cell loss or clinical T1D onset. One hypothesis is that the process affecting the exocrine compartment leads ultimately to β cell loss ( 68 , 69 ). How the reduced pancreas volume affects exocrine function in individuals with T1D is not well defined, but reduced pancreatic function (fecal elastase) and pancreatic exocrine insufficiency have been reported in some individuals with T1D ( 61 , 63 ).

There is emerging consensus that considerable heterogeneity exists on many levels within the clinical phenotype of T1D ( 3 , 70 ). T1D heterogeneity should not be confused with other causes of insulin deficiency such as certain forms of monogenic diabetes, immune checkpoint–related diabetes, or rare monogenic causes of immune-related diabetes ( 71 – 75 ) that can clinically mimic T1D, but instead refers to clinical, immunological, metabolic, and/or pathological heterogeneity in individuals when the clinical diagnosis of T1D seems likely ( Table 2 ). For example, there is heterogeneity in age of onset, rate of disease progression (decline in β cell function and mass), residual C-peptide production ( 27 , 36 , 37 , 50 , 76 – 79 ), a spectrum of immunological signatures (autoantibody, T cell signatures, innate immunity, etc.; refs. 80 – 84 ), and pathological abnormalities ( 29 , 31 , 49 , 85 , 86 ). Residual β cells, as reflected by low levels of C-peptide, persist for years after onset of hyperglycemia in some individuals with T1D ( 35 , 56 , 57 ). Since it is not currently possible to noninvasively assess β cell mass by imaging or biomarkers other than C-peptide, defining the natural history of β cell loss or the impact of interventions to sustain or improve β cell mass is problematic. For example, although the decline in β cell mass in Figure 1 is shown as a gradual, smooth decline, this is mostly speculative and it is possible that the decline may stop and restart, may stop and remain flat, etc. Using a nomenclature from other diseases with clinical heterogeneity such as asthma, some have proposed that T1D consists of multiple “endotypes,” meaning different underlying pathogenic processes that produce a similar phenotype ( Table 2 ) ( 70 ).

Examples of T1D heterogeneity

Two other examples of this heterogeneity are adult-onset T1D (onset after 20 years of age) and a form of diabetes sometimes termed latent autoimmune diabetes in adults (LADA). In the former, individuals have similar phenotypic features to T1D with onset in childhood or adolescence (non-obese, islet-directed autoantibodies, insulin deficiency, glycemic lability, etc.), but fewer CD20 + B lymphocytes in the few pancreata that have been studied, and a presumably slower loss of β cell mass. In contrast, LADA is often clinically confused with T2D, with onset in middle age, obesity, initial insulin independence then progressing to insulin-dependence, and presence of autoantibodies (often autoantibodies only to GAD in distinction to T1D of childhood or adolescent onset) ( 87 , 88 ). The genetics of LADA show similarities, but not complete overlap, with genetic loci associated with T1D and in some series, the T2D genetic risk loci TCF7L2 is linked ( 89 , 90 ). Whether this variability in genetic loci reflects imprecise phenotyping and inclusion of individuals with T2D is not clear. Critical unanswered questions are whether T1D with onset in the first or second decade of life is the same disease as T1D with onset in older adolescents or young adults, and how these relate to LADA with its onset in middle age.

Current T1D therapy focuses on matching exogenous insulin and food intake while incorporating daily activities such as exercise and sleep. Remarkable advances have been made in insulin formulation and diabetes technology, including methods for insulin delivery and glucose monitoring ( 91 – 97 ). Additionally, T1D clinical care is evolving to incorporate mobile technology (smart phones, wearable devices, telemedicine, etc.; ref. 91 ) and provide greater emphasis on behavioral and psychosocial aspects, the social determinants of health, and health care access/cost. The goal is near normoglycemia while avoiding hypoglycemia and allowing for normal daily activities. Traditionally, the glycemic goal in T1D has been an A1C of 7.0 or lower, with this target individualized for age, comorbidities, and lifestyle ( 98 ). Importantly, hypoglycemia, a major adverse effect of intensive glycemic control, is a substantial, lifelong burden of current therapy for T1D ( 99 ).

The foundation of insulin therapy for T1D is a combination of basal insulin and bolus insulin associated with nutrient or caloric consumption. Insulin can be given by syringe, pen (including “smart” pens), catheter connected to insulin pump or an insulin delivery device (continuous subcutaneous insulin injection), or more rarely by inhalation ( 92 , 94 , 100 ). By manipulating the insulin amino acid sequence or by incorporating attachments to the insulin protein (e.g., fatty acid, additional amino acids), the absorption profile of injected insulin can be greatly prolonged or accelerated compared with native insulin protein in a neutral buffer. In addition, insulin diluents can be used to delay absorption (such as protamine in neutral protamine Hagedorn plus native insulin) or accelerate absorption (L-arginine and niacinamide plus insulin aspart analog) ( 92 ). Because of the importance of basal insulin in regulating hepatic glucose output, basal insulin formulations with action duration of more than 40 hours or up to a week have recently become available ( 92 , 101 ). An advantage of longer-acting formulations is reduced frequency of severe hypoglycemia. Based on decades of research to understand insulin structure and function ( 102 – 104 ), future possibilities include glucose-responsive insulin ( 105 ). The price of insulin formulations in the United States has risen dramatically over the past decade, placing a tremendous financial burden on individuals with T1D ( 106 , 107 ).

These remarkable advances in insulin formulations and insulin delivery devices have greatly improved clinical care. However, many individuals with T1D state that continuous glucose monitoring (CGM) has had an even greater and more dramatic impact on their daily lives. Individuals with T1D for more than 40 years have lived through dramatic transitions from using urine glucose to infer blood glucose over past hours, to finger-stick capillary blood glucose measurements at multiple times a day, to CGM providing an essentially unlimited number of glucose values a day. Although A1C remains the standard for diabetes diagnosis and a predictor of diabetes-related complications, CGM and its dense glycemic datasets allow glycemic metrics, such as the ambulatory glucose profile using time in a defined glycemic range (TIR), the glucose management indicator (GMI) based on the mean glucose and correlative with A1C, glycemic variability or coefficient of variation, and the amount of time in the hypoglycemic range ( 98 , 108 ). These metrics correlate with A1C, are changing how the provider and patient adjust insulin, and are empowering individuals with T1D with information to better organize their daily living routines.

The rapidly evolving CGM technology is currently based on a sensor or electrode detecting the electrochemical product (e.g., hydrogen peroxide) of the reaction between interstitial glucose and a glucose oxidase. Importantly, all current CGM technologies measure interstitial glucose, which, while in equilibrium with blood glucose, may lag behind or differ from blood glucose, especially when the blood glucose is changing rapidly ( 98 , 108 – 110 ). Currently, the two broad categories of CGM are real-time CGM, in which interstitial glucose is monitored and sent to the recording device essentially continuously, and intermittent CGM, in which the sensor is in place continuously, but the glucose is only recorded when the detector is placed over the sensor. Both approaches use a subcutaneously placed sensor that must be replaced every 3–14 days; an implanted sensor that must be replaced every 6 months is also available. Although the TIR and GMI are critical CGM outputs, other features such as rate of glucose change, glucose trends, hypoglycemia alarms, and suspension of insulin delivery are extremely valuable, enabling the individual with T1D to respond preemptively and avoid anticipated hyper- or hypoglycemia and to adapt diabetes self-management in terms of diet or activities such as exercise. The pairing of CGM and an insulin delivery device or a closed loop system where the sensor-derived data regulate insulin delivery is discussed below.

Diabetes-related complications, both microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular (cardiovascular disease, peripheral artery disease), are responsible for the considerable morbidity and mortality in T1D. Although hyperglycemia duration and severity are the drivers of diabetes-related complications in T1D, the molecular mechanisms of how excess glucose leads to a specific organ dysfunction are incompletely defined and likely distinctive for the affected organ system. Diabetes-related complications are likely multifactorial and involve genetic susceptibility to glucose exposure, epigenetic changes induced by hyperglycemia, associated dyslipidemia, and cellular pathways such as advanced glycation end products, sphingolipid metabolism (neuropathy), cytokines, multiple growth factors (VEGF in retinopathy), and/or oxidative stress ( 111 – 114 ). Prevention of diabetes-related complications by intensive glycemic control is a major focus of T1D clinical care but is challenging because the complications occur years or decades after T1D onset. Improved glycemic control does not reverse established diabetes-related complications.

Long-term outcomes in individuals with T1D have greatly improved with reduced frequency of retinopathy, nephropathy, and neuropathy and improved cardiovascular outcomes; it is becoming increasingly common for individuals to live for more than 50 years with T1D ( 115 ). This is partly due to improvements in glycemic control as demonstrated in the Diabetes Control and Complications Trial (DCCT) ( 116 ) and partly because of improvements in lipid and blood pressure management or treatments that reduce or delay nephropathy or retinopathy. The DCCT also demonstrated that intensive glycemic control (near normoglycemia) begun at T1D onset partially preserves β cell function (and presumably β cell mass) as reflected by C-peptide secretion, subsequently leading to improved glycemic control and less hypoglycemia. However, hypoglycemia is a major adverse effect of intensive glycemic control (increased 3-fold in DCCT) and a substantial, lifelong burden for individuals with T1D ( 99 ). Individuals with T1D using intensive glycemic therapy also gained more weight. In the years after the intensive glycemic control phase of the DCCT, study participants in the intense glycemic or control groups were followed as part of the Epidemiology of Diabetes Interventions and Complications (EDIC) study. Both groups in this next phase had similar glycemic control (A1C ~8%), but strikingly, the incidence of complications between the intense treatment groups further widened in the two groups, suggesting that the period of improved glycemic control during the intensive treatment phase translated into a continued and sustained reduction in microvascular complications ( 116 ). Termed “metabolic memory,” this also translated into improvement in cardiovascular outcomes ( 117 – 119 ). Although some have disputed the concept of metabolic memory ( 120 ), it seems likely that early glycemic control has long-term benefits, perhaps lasting more than a decade, maybe through epigenetic mechanisms ( 116 ). The clear clinical implication is that glycemic control as close to normoglycemia as safely possible should be the goal beginning immediately after the onset of hyperglycemia.

The management of T1D-related nephropathy, retinopathy, neuropathy, and cardiovascular disease continues to improve with clear benefits of ACE inhibitors and angiotensin receptor blockers in slowing the decline in glomerular filtration rate, anti-VEGF therapies affecting diabetic macular edema and retinopathy, and possibly sodium-glucose cotransporter-2 (SGLT-2) inhibitors or GLP-1 receptor agonists in T1D-related cardiovascular disease ( 111 , 121 , 122 ). Still, some individuals develop debilitating or life-threatening complications such as end-stage renal disease or neuropathy ( 122 – 125 ). Although intense glycemic control clearly reduces microvascular complications, other factors, such as genetic susceptibility, residual insulin secretion, activity of selected glycolytic enzymes, advanced glycation end-product production, and degree of activation of the renin–angiotensin–aldosterone system, may explain the difference in the rates of complications across the T1D population ( 115 , 125 ). A recent estimate predicts a lifespan in individuals with T1D and near-normal glycemic control similar to the general population, a remarkable change from past outcomes ( 126 , 127 ).

At this dawn of the next century after insulin’s discovery, anticipated new therapies fall into three broad categories ( Figure 3 ): (a) exogenous insulin replacement; (b) cell-based insulin delivery from new sources of insulin-producing cells; and (c) protection of endogenous β cells by immunomodulation.

Emerging or future T1D therapies. ( A ) Exogenous insulin replacement includes insulin analogs designed to optimize absorption, integrated closed-loop systems combining insulin delivery devices and glucose-sensing technology, and personalized algorithms (AI, artificial intelligence) to tailor insulin replacement. ( B ) Cell-based insulin delivery options include transplantation of islets or insulin-producing cells (derived from ES or iPS cells), strategies to stimulate β cell proliferation or regeneration, and approaches that encourage transdifferentiation of host cells into insulin-producing cells. ( C ) Protective strategies include immunomodulatory approaches to block inflammatory cytokines or pathogenic immune cells and prevent damage or loss of β cells. See text for additional information.

The combination of a glucose sensor and an automatically adjusting insulin delivery device in a closed loop system, often imprecisely termed an “artificial pancreas,” is very effective in T1D in children, adults, and pregnancy ( 93 , 128 – 131 ). This hardware/software is being further improved by emerging algorithms to “correct” interstitial glucose to the actual blood glucose and to predict insulin dosing based on artificial intelligence–based interpretations of personalized glucose excursions and activity. Future improvements in sensor technology may include measuring glucose at other body sites (e.g., eye, skin) or optical/vascular approaches to assess blood rather than interstitial glucose. Improvements in insulin administration are needed as subcutaneous insulin absorption into the vascular system is considerably slower than physiological insulin secretion even with modified insulin analogs ( 92 ). Plus, insulin, delivered peripherally and not into the portal vein, leads to reduced insulin action at the liver; it remains unclear how close to “normal” glucose homeostasis can be achieved by insulin delivery at a peripheral site, which also induces insulin resistance as an additional challenge ( 132 ). Devices that also deliver glucagon may be part of future therapies ( 133 ).

Cell-based insulin delivery may include transplantation of insulin-producing cells, transdifferentiation of other pancreatic cell types (exocrine, ductal, or α) into insulin-producing cells ( 134 , 135 ), or amplification/regeneration of endogenous β cells ( 136 , 137 ) ( Figure 3 ). The combination of islet allotransplantation, normal human islets from cadaveric donor(s) infused into the portal vein, and immunosuppression to prevent allo- and autoimmunity is effective in reducing hypoglycemia frequency in T1D, sometimes leading to independence from exogenous insulin ( 138 – 142 ). Because human islet supply is quite limited, insulin-producing cells from embryonic stem cells (ES cells), human induced pluripotent stem cells (iPS cells), or even xenografts (porcine) are also under investigation ( 139 , 143 – 148 ). Considerable progress in ES and iPS cell differentiation into insulin-producing cells will soon lead to clinical trials and offer the future possibility of transplanting insulin-producing cells modified to resist allo- and autoimmunity. The optimal site for transplantation of islets or insulin-producing cells is uncertain. Transdifferentiation and regeneration strategies, although attractive, need more research before testing in humans. Glucose- and nutrient-regulated insulin secretion from these new sources of insulin-producing cells is incompletely defined, but crucial. Particularly critical is the appropriate cessation of insulin secretion when the blood glucose is in the normal range to avoid hypoglycemia.

A number of studies, often part of TrialNet ( 149 ) or the Immune Tolerance Network ( 150 ), suggest that survival of endogenous β cells remaining in early stage 3 T1D can be enhanced and partially protected by immunomodulatory approaches directed at B lymphocytes, T lymphocytes, regulatory T cells, or inflammation by anti-CD3, anti-CD20, CTLA-4Ig, anti-CD2, anti–IL-1, anti–TNF-α, or thymoglobulin ( 39 , 151 – 153 ). However, thus far, such immunomodulatory interventions given soon after hyperglycemia onset have had the modest impact of preserving C-peptide secretion, which is not often sustained. Efforts are underway to understand why only some individuals respond to such therapies, if repeat dosing is needed, and if antigen-specific therapy or exposure (insulin, GAD) can reduce the autoimmune response ( 39 , 154 ). A single course of an Fc receptor–nonbinding anti-CD3 monoclonal antibody given to first-degree relatives at high risk for developing T1D (stage 2, two islet-directed autoantibodies) delayed the onset of hyperglycemia by 2 years, with twice as many individuals progressing to clinical diabetes in the control group (57% versus 28%) ( 155 ). This intervention is thought to target “pathogenic” T cells, raising the possibility of induction of immune tolerance or a reset of the immune system ( 156 ). This study also highlights how current efforts in the United States, Europe, and Australia to identify high-risk individuals by screening the general population for T1D-GRS and/or islet-directed autoantibodies may lead to new efforts to prevent T1D ( 154 ). Efforts to prevent T1D would also be greatly accelerated by identification of the triggering event(s) for β cell–directed autoimmunity.

Over the next decade, it truly will be a competition between these three clinically viable therapeutic options for T1D. For illustrative purposes, consider these scenarios in which glycemia is essentially normalized and diabetes-related complications are prevented by the following: (a) a mechanical insulin-delivery device/glucose sensor system, requiring little input from the patient and with little limitation of daily diet or activities; (b) transplantation of insulin-producing cells every 1–2 years, accompanied by continuous, “safe” immunomodulation/immunosuppression; and (c) intermittent, immunomodulation/immunosuppression beginning at the onset of hyperglycemia or prior to hyperglycemia (stage 2).

What will be the therapy for T1D in 5 years, 10 years, or 20 years? This will be determined by balancing the benefit, sustainability, adverse impact, safety, convenience, personal preference, and financial cost of these evolving options ( 39 , 157 – 159 ). Plus, as therapies in each category continue to improve, the “bar is raised” for other competing therapies and the optimal therapy will likely change. In addition, an individual’s age and T1D duration will be important considerations in choosing among competing therapies — the choice will likely be different in a 12-year-old with new-onset T1D and a 42-year-old with T1D for 30 years.

In summary, since insulin’s discovery, there has been much progress in understanding the pathogenesis of T1D and in using insulin to improve the lives of individuals with T1D. These efforts are incomplete in that T1D continues to be a substantial burden for individuals with T1D and their families. Hopefully, the opportunities discussed here will be realized, leading to the prevention of T1D and its associated burdens.

The author apologizes to the many investigators whose important work on T1D was not cited because of space limitations. Research by the author and his colleagues is or has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases; the Human Islet Research Network (RRID: SCR_014393; https://hirnetwork.org; DK112232, DK123716, DK123743, DK104211, DK108120); and by DK106755, DK117147, DK20593 (Vanderbilt Diabetes Research and Training Center); the Leona M. and Harry B. Helmsley Charitable Trust; the Juvenile Diabetes Research Foundation; and the Department of Veterans Affairs (BX000666). The author thanks Vanderbilt colleagues, Daniel J. Moore, John T. Walker, and Jordan J. Wright, for reading the manuscript and providing insightful suggestions.

Conflict of interest: The author has declared that no conflict of interest exists.

Copyright: © 2021, American Society for Clinical Investigation.

Reference information: J Clin Invest . 2021;131(8):e142242.https://doi.org/10.1172/JCI142242.

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Your top priorities for research into type 1 diabetes revealed

Hundreds of people with type 1 diabetes, their families and healthcare professionals have chosen their most pressing research priorities for type 1 diabetes. The top ten priorities will help to guide future type 1 diabetes research in the UK and Ireland to make sure it has the greatest possible benefit for people with the condition. 

As the UK’s largest charitable funder of diabetes research, it’s critical that our research funds address the specific challenges and needs of people with diabetes and those who care for them.

It’s also critical that we make sure others - academics, healthcare professionals and other research funders – hear these views loud and clear and act upon them.  

We work with the Priority Setting Partnership (PSP) initiative, run by the  James Lind Alliance (JLA) and supported by the National Institute for Health Research (NIHR) , to help bring the views of people with real-life experience of diabetes into research.

Through surveys and workshops, this initiative finds and prioritises their most pressing concerns and questions that can be answered through research. Diabetes UK contributed to the first Type 1 Diabetes PSP in 2011, the Diabetes and Pregnancy PSP in 2020 , and led the Type 2 Diabetes PSP in 2017. 

Your new top ten priorities  

Since the last Type 1 Diabetes PSP in 2011 there's been  some big changes in type 1 treatment and care , so the priorities were due an update. The latest Type 1 PSP – which condensed and whittled down nearly 3000 questions submitted by people affected by type 1 to a shortlist of the top ten – has just been published . And here they are: 

1. Can the use of artificial intelligence or faster acting insulins help achieve fully closed loop insulin delivery?

2. Is time in range a better predictor of diabetes management and complications compared to HbA1c (an average reading of blood sugar over a 3-month period)?

3. What impact do hormonal phases such as the perimenstrual period and menopause play in glycaemic management and what treatments are most effective for managing glucose levels around these times?

4. What interventions are the most effective for reducing diabetes related distress and burnout?

5. What are the long-term implications of frequent hypoglycaemia on physical and mental health?

6. What impact does type 1 diabetes (including frequent low blood sugar) have on memory and cognition in older adults?

7. How can health care professionals better take into account the physical, psychological and social aspects of type 1 diabetes in clinics?

8. How can access to potential therapies like stem cell therapy, transplants and medications that modify the immune systems be improved so that everyone with type 1 diabetes can be guaranteed access?

9. Why do some people with type 1 diabetes become insulin resistant and does resistance increase with the number of years a person has diabetes and if so, why?

10. Can technology assist to accurately count carbohydrates without having to weigh or measure all foods and drink? 

Dr Christine Newman, Lead Clinical Researcher at the  Health Research Board Diabetes Collaborative Clinical Trial Network  in Ireland who funded the PSP, emphasised the importance of these findings:

“This study is a powerful example of how Public and Patient Involvement can shape the future of healthcare. This work highlights the real-world challenges and unmet needs of adults living with Type 1 diabetes. By focusing on these top ten priorities, we can ensure that future research and healthcare services are aligned with what truly matters to those affected by the condition.”

We will use these top 10 research priorities in the decisions it makes about how research is funded, and they will inform the work of the Diabetes Research Steering Groups .

We will also publicise these priorities widely to researchers and organisations that fund diabetes research. The priorities could influence those who work in universities and academic institutions, government agencies or in industry.  

Dr Elizabeth Robertson, Director of Research at Diabetes UK, explains: 

“We need to make sure research that we fund has the greatest possible benefit for people with diabetes. Knowing the most important priorities of people living with or treating type 1 diabetes will help us direct funding to where it’s needed most.”

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• Review Article
• Published: 26 June 2018

Technology in the management of type 1 diabetes mellitus — current status and future prospects

• Martin Tauschmann 1 , 2 &
• Roman Hovorka   ORCID: orcid.org/0000-0003-2901-461X 1 , 2  

Nature Reviews Endocrinology volume  14 ,  pages 464–475 ( 2018 ) Cite this article

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• Diabetes complications
• Outcomes research
• Type 1 diabetes

Type 1 diabetes mellitus (T1DM) represents 5–10% of diabetes cases worldwide. The incidence of T1DM is increasing, and there is no immediate prospect of a cure. As such, lifelong management is required, the burden of which is being eased by novel treatment modalities, particularly from the field of diabetes technologies. Continuous glucose monitoring has become the standard of care and includes factory-calibrated subcutaneous glucose monitoring and long-term implantable glucose sensing. In addition, considerable progress has been made in technology-enabled glucose-responsive insulin delivery. The first hybrid insulin-only closed-loop system has been commercialized, and other closed-loop systems are under development, including dual-hormone glucose control systems. This Review focuses on well-established diabetes technologies, including glucose sensing, pen-based insulin delivery, data management and data analytics. We also cover insulin pump therapy, threshold-based suspend, predictive low-glucose suspend and single-hormone and dual-hormone closed-loop systems. Clinical practice recommendations for insulin pump therapy and continuous glucose monitoring are presented, and ongoing research and future prospects are highlighted. We conclude that the management of T1DM is improved by diabetes technology for the benefit of the majority of people with T1DM, their caregivers and guardians and health-care professionals treating patients with T1DM.

Innovations in technologies have greatly benefited diabetes management.

Flexible ways of delivering insulin, such as insulin pump therapy, are increasingly popular.

Minimally invasive real-time continuous glucose monitoring is progressing towards accurate, insulin-dosing approved, factory-calibrated systems and has become part of standard care for people with type 1 diabetes mellitus in many countries.

Automated glucose-responsive insulin delivery systems, including threshold-based suspend, predictive low glucose management insulin pump therapy and hybrid closed-loop systems, offer means for further improvements in glycaemic control while reducing hypoglycaemia exposure.

Data management tools and applications for diabetes self-management are helping people with type 1 diabetes mellitus and health-care professionals to manage intricate, extensive data created by diabetes technologies.

Advances in bihormonal closed-loop technology, bioartificial pancreas systems and smart insulin might further improve the care and management of people with type 1 diabetes mellitus in the future.

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Acknowledgements

The authors acknowledge the support of the National Institute of Health Research Cambridge Biomedical Research Centre, the Efficacy and Mechanism Evaluation National Institute for Health Research (#14/23/09), The Leona M. and Harry B. Helmsley Charitable Trust (#2016PG-T1D045), Wellcome Strategic Award (100574/Z/12/Z), JDRF (#2-SRA-2014-256-M-R), the National Institute of Diabetes and Digestive and Kidney Diseases (1UC4DK108520-01) and Diabetes UK (#14/0004878).

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The narrative is based on results from clinical studies and meta-analyses known to the authors. Relevant studies were identified by searching PubMed and Web of Science for articles on each of the technologies up to November 2017, as well as using cited literature in retrieved articles. Information relevant to children, adolescents and adults with type 1 diabetes mellitus was considered; studies in pregnancy complicated by diabetes were excluded. Priority was given to meta-analyses and systematic reviews.

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R.H. has received speaker honoraria from Eli Lilly, Novo Nordisk and Astra Zeneca, has served on the advisory panel for Eli Lilly and Novo Nordisk, has received licence fees from B. Braun and Medtronic, has served as a consultant to B. Braun and has patents and patent applications related to closed-loop systems. M.T. has received speaker honoraria from Medtronic and Novo Nordisk.

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Tauschmann, M., Hovorka, R. Technology in the management of type 1 diabetes mellitus — current status and future prospects. Nat Rev Endocrinol 14 , 464–475 (2018). https://doi.org/10.1038/s41574-018-0044-y

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Type 1 diabetes

Linda a dimeglio.

Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA;

Carmella Evans-Molina

Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA;

Richard A Oram

Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK

Contributors

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.

Introduction

At first consideration, type 1 diabetes pathophysiology and management might seem straightforward; however, the more that is learnt about the disease, the less it seems is truly known. Improved understanding of the disease’s pathogenesis has not led to a single unifying Koch’s postulate for all cases. What once seemed like a single autoimmune disorder, with roots in T-cell mediated attack of insulin-producing β cells, is now recognised to result from a complex interplay between environmental factors and microbiome, genome, metabolism, and immune systems that vary between individual cases.

Despite known genetic underpinnings, most people who are diagnosed with type 1 diabetes do not have a relative with the disease or even the highest risk combination of HLA alleles, making attempts at primary disease prevention difficult. Although survival and patient health have improved considerably, particularly in the past 25 years, a cure for type 1 diabetes remains elusive. 1 , 2 Additionally, despite advances in technology, glycaemic control for most people with type 1 diabetes is not optimised, and many cannot access modern therapies because of the high costs of even basic care.

In 1984, George Eisenbarth developed a conceptual model for the pathogenesis of type 1 diabetes that is still used nowadays ( figure 1 ). 3 The model plots β-cell mass against age, highlighting an event sequence starting with predisposing genetic risk, then a precipitating environmental trigger that causes islet-specific auto-immunity, followed by β-cell loss, dysglycaemia, clinical diabetes, and rapid progression to complete β-cell loss. Although useful, this model does not address the increasingly apparent complexity of type 1 diabetes pathogenesis. Additionally, the disease pathogenesis is shown by a single line of disease course over time; however, at all stages of the disease heterogeneity exists that is not well understood.

Key events of the Eisenbarth model 3 over the course of the disease (measured in years) are shown by dotted lines at different time points. Challenges to this model, taking into account the increasing complexity of type 1 diabetes, include the following: precipitating immune events that might occur prenatally (A); large variation in starting β-cell mass and function, defects in one or both could be developmentally programmed (B); initiation of autoimmunity is measured by autoantibodies, but other immunological abnormalities probably precede the presence of detectable pancreatic antibodies (C); the patient’s environment could affect their entire disease course (D); β-cell loss could relapse or remit (E); dysglycaemia occurs before clinical diagnosis (F); decline in β-cell function might not mirror decline in β-cell mass—methods to measure β-cell mass have not been established (G); and residual C-peptide is detectable in many people who have long duration type 1 diabetes (H). Furthermore, progression through stages A–C is heterogeneous, and will be affected by immune, genetic, environment, and key demographic features (ie, age, body-mass index). Adapted from Atkinson et al. 4

This Seminar provides a review of type 1 diabetes and the status of research in the field. We focus on developments from the past 5 years that highlight the heterogeneity and complexity of the disease.

A diagnosis of diabetes is based on a fasting blood glucose concentration above 7·0 mmol/L (126 mg/dL), a random blood glucose concentration above 11·1 mmol/L (200 mg/dL) with symptoms, or an abnormal result from an oral glucose tolerance test. 5 In the absence of symptoms, abnormal glycaemia must be present on two different occasions. A diagnosis of diabetes can also be made on the basis of a glycated haemoglobin (HbA 1c ) concentration above 48 mmol/mol (6·5%). However, since dysglycaemia progression can be rapid in patients with type 1 diabetes, HbA 1c is less sensitive for diagnosis than fasting or stimulated blood glucose measurements. 5

Children with type 1 diabetes commonly present with symptoms of polyuria, polydipsia, and weight loss; approximately a third present with diabetic ketoacidosis. 6 The onset of type 1 diabetes can be more variable in adults, who might not present with the classic symptoms seen in children. Although traditional definitions classified type 1 diabetes as juvenile onset, the disease can occur at any age, with up to 50% of cases occurring in adulthood. 7 As many as 50% of adults with type 1 diabetes might be initially misclassified as having type 2 diabetes. 8 Similarly, in conjunction with the epidemic of childhood obesity, type 2 diabetes is increasingly common in adolescents (particularly in non-white individuals), and monogenic diabetes (eg, maturity diabetes onset of the young) accounts for 1–6% of childhood diabetes cases. 9 – 11

Although low C-peptide concentration as a marker of severe endogenous insulin deficiency is useful to guide both classification and treatment in cases of diabetes assessed over 3 years after clinical diagnosis, 12 no single clinical feature can perfectly distinguish type 1 from non-type 1 diabetes at diagnosis. Classification depends on an appreciation of other risk factors for type 1 versus other subtypes and the integration of clinical features (eg, age of diagnosis and body-mass index) with biomarkers (eg, pancreatic autoantibodies). 13

Over 90% of people with newly diagnosed type 1 diabetes have measurable antibodies against specific β-cell proteins, including insulin, glutamate decarboxylase, islet antigen 2, zinc transporter 8, and tetraspanin-7. 14 Birth cohort studies 15 , 16 of individuals with a high genetic risk for diabetes have shown a peak incidence of first autoantibody development before age 2 years. Most people with a single autoantibody do not progress to type 1 diabetes, but seroconversion to the presence of two or more serum autoantibodies in children is associated with an 84% risk of clinical type 1 diabetes by the age of 18 years ( figure 2A ). 16 The high risk of progression in the presence of multiple autoantibodies has led to a redefining of type 1 diabetes stages. In this new paradigm, a preclinical stage 1 case of type 1 diabetes is defined as the presence of two or more autoantibodies, while stages 2 and 3 are defined as the progression of metabolic abnormalities from abnormal glycaemia to overt diabetes, diagnosed by standard criteria ( figure 2B ). 18 Since the progression from islet autoantibody positivity to clinical diabetes could take months or years, defining multiple auto-antibody positivity as stage 1 allows targeting of immune interventions to a realistic primary outcome and facilitates early life intervention studies. 19

(A) The probability of developing diabetes in childhood stratified by the number of islet antibodies. In a study by Zeigler and colleagues, 16 13 377 children were identified as at risk in the newborn or infant period on the basis of high-risk HLA genotypes or having a relative with type 1 diabetes, or both, and were followed-up regularly. The numbers at risk are the number of children receiving follow-up at ages 0, 5, 10, 15, and 20 years. Adapted from Ziegler et al 16 with permission of the American Medical Association. (B) Type 1 diabetes progression and stages of type 1 diabetes. Stage 1 is the start of type 1 diabetes, marked by individuals having two or more diabetes-related autoantibodies and normal blood sugar concentrations. In stage 2, individuals have dysglycaemia without symptoms. Stage 3 is the time of clinical diagnosis. Reproduced from Greenbaum et al, 17 with permission from the American Diabetes Association. T1D=type 1 diabetes.

Type 1 diabetes is a heritable polygenic disease with identical twin concordance of 30–70%, 20 sibling risk of 6–7%, and a risk of 1–9% for children who have a parent with diabetes. 21 The overall lifetime risk varies greatly by country and geographical region but overall is around one in 250 people. 22 The disease is slightly more common in men and boys than in women and girls. 23 Two HLA class 2 haplotypes involved in anti gen presentation, HLA DRB1*0301-DQA1*0501-DQ*B10201 ( DR3 ) and HLA DRB1*0401-DQA1*0301-DQB1*0301 (DR4-DQ8) , are linked to approximately 50% of disease heritability and are prevalent in white people. 24 Other haplotypes are known to reduce type 1 diabetes risk, including DRB1*1501-DQA1*0102-DQB1–0602 ( DR15-DQ6 ). 24 The mechanisms by which these HLA haplotypes interact and alter risk are not completely understood. Different HLA associations in other racial groups are recognised but remain poorly characterised. 24 Genome-wide association studies have identified over 60 additional non-HLA loci associated with the risk of type 1 diabetes. These variants have been predominantly associated with the immune system and highlight pathways that are important in disease development—eg, insulin gene expression in the thymus, regulation of T-cell activation, and viral responses. 24 These HLA and non-HLA genetic associations could identify potential targets for future disease-modifying therapies or subgroups of patients who could benefit from specific immune interventions.

Historically, people at high risk of type 1 diabetes have been identified for research by HLA risk or familial risk, or both. 25 By contrast, individual non-HLA loci cannot be used to predict type 1 diabetes or discriminate it from other types of diabetes. Combined measurement of HLA and non-HLA loci into genetic risk scores could offer improved prediction of the risk of developing type 1 diabetes and discrimination of type 1 from type 2 diabetes. 26 , 27 Furthermore, the continuing fall of genotyping costs could facilitate future population-level disease prediction by use of genetic risk scores. 19 , 28

Epidemiology

Globally, type 1 diabetes is increasing both in incidence and prevalence, with overall annual increases in incidence of about 2–3% per year. 29 , 30 US data 31 suggest an overall annualised incidence from 2001 to 2015 of about 22·9 cases per 100 000 people among those younger than 65 years; data from other regions suggest similar incidences. 32 The greatest observed increases in incidence of type 1 diabetes are among children younger than 15 years, particularly in those younger than 5 years. 33 These increases cannot be explained by genetic changes, implicating environmental or behavioural factors, or both. Many environmental exposures are associated with type 1 diabetes, including infant and adult diet, vitamin D sufficiency, early-life exposure to viruses associated with islet inflammation (eg, enteroviruses), and decreased gut-microbiome diversity. 34 Obesity is associated with increasing presentation of type 1 diabetes, with β-cell stress potentially providing a mechanistic underpinning. 34 , 35 The large differences in the incidence of type 1 diabetes in genetically similar populations that are separated by socioeconomic borders 36 and the increasing incidence of type 1 diabetes in genetically low-risk individuals 37 highlight the importance of environmental risk factors regardless of genetic background risk. Further work is being done to understand the role of gene–environment interactions in the pathogenesis of type 1 diabetes, the role of different loci and pathways at different stages of the disease, and whether loci that are independent of disease risk could have a role in disease progression after development of autoimmunity. 38 – 40 Some data 31 , 41 suggest that the observed incidence could be declining in adults or potentially even levelling off across all age ranges; worldwide registry data will eventually reveal if this pattern is indeed true. 42

The incidence of type 1 diabetes varies by country and by region within countries. 31 At northern latitudes, people born in the spring are more likely to develop the disease than those born in the other seasons. 43 The peak incidence of diagnosis is seen in children aged 10–14 years. 31 , 32 Although many people present with type 1 diabetes in adulthood, 44 the higher incidence of type 2 diabetes in adulthood compared with type 1 diabetes and the flawed criteria for distinguishing these forms of disease make assessment of the incidence of type 1 diabetes in adults very difficult. 23 , 45 Most people living with type 1 diabetes are adults. 46

The immune phenotype of type 1 diabetes

The pathogenesis of type 1 diabetes results from a complex interaction between the pancreatic β-cell and innate and adaptive immune systems ( figure 3 ). 47 The question of whether a trigger for the immune response against β cells exists or whether the immune response is a random stochastic event has been a subject of considerable speculation and controversy. Several viral infections are associated with type 1 diabetes, with enterovirus being one of the most commonly associated infections. Enteroviral major capsid protein VP1 and RNA have been detected in islets from people with recent-onset type 1 diabetes, 48 along with hyper-expression of the class 1 major histo compatibility complex 49 and other indices of viral infection. One possibility is that some people with type 1 diabetes have an atypical, chronic viral infection of β cells, leading to chronic inflammation and the development of autoimmunity. The viral hypothesis has been difficult to test, although both antiviral therapy and the development of vaccines targeting enteroviruses are being pursued for this purpose.

The development of type 1 diabetes is thought to be initiated by the presentation of β-cell peptides by antigen-presenting cell (APCs). APCs bearing these autoantigens migrate to the pancreatic lymph nodes where they interact with autoreactive CD4+ T lymphocytes, which in turn mediate the activation of autoreactive CD8+T cells (A). These activated CD8+ T cells return to the islet and lyse β cells expressing immunogenic self-antigens on major histocompatibility complex class I surface molecules (B). β-cell destruction is further exacerbated by the release of proinflammatory cytokines and reactive oxygen species from innate immune cells (macrophages, natural killer cells, and neutrophils; C). This entire process is amplified by defects in regulatory T lymphocytes, which do not effectively suppress autoimmunity (D). Activated T cells within the pancreatic lymph node also stimulate B lymphocytes to produce autoantibodies against β-cell proteins. These autoantibodies can be measured in circulation and are considered a defining biomarker of type 1 diabetes (E).

In the field, much effort has been given to the study of the adaptive immune system in type 1 diabetes by use of assays of peripheral lymphocytes selected for autoreactivity to islet antigens. Increased frequency of islet-specific autoreactive CD8+ T lymphocytes and decreased regulatory immune function have been associated with type 1 diabetes. 50 Experiments, such as the transfer of type 1 diabetes following non-T-cell depleted allogeneic bone-marrow transplantation, 51 development of type 1 diabetes in an individual with B-lymphocyte and antibody deficiency, 52 and inherited genetic defects of T-lymphocyte function causing type 1 diabetes 53 highlight the crucial role of T cells in the pathophysiology of type 1 diabetes. 54 Almost all studies of peripheral autoimmunity in people with type 1 diabetes show overlap of phenotypes seen in the general population, and the proportion of islet autoreactive cells present in the periphery is often tiny (only a few cells among millions of non-autoreactive cells). As a result, connecting the population of autoreactive immune cells that is detectable in blood to the disease process in islets has been difficult. A key development has been the isolation of T lymphocytes that are reactive to β-cell antigen peptides from islets of organ donors with type 1 diabetes. 55 – 57

Histopathologically, these processes are observed as insulitis or immune-infiltrated (insulitic) islets. 58 CD8+ T lymphocytes are the most common immune cells within insulitic lesions, with CD4+ T cells present in lower numbers. Distinct patterns of insulitis that stratify with the aggressiveness of β-cell loss and age of diagnosis have been identified in insulitic islets. 59 Although insulitis is common and intense in animal models of type 1 diabetes, it is much rarer and more variable in human beings ( figure 3 ). 60

The β-cell phenotype of type 1 diabetes

At diagnosis, people with type 1 diabetes have reduced β-cell function compared with healthy controls. 61 With amelioration of hyperglycaemia, these β cells can have a partial recovery of insulin secretory function, leading to a so-called honeymoon period after diagnosis with minimal or no exogenous insulin needed. Over time, many of these residual cells are lost. However, analysis of pancreatic sections from individuals with long-term type 1 diabetes show the presence of residual β cells decades after diagnosis. 62 , 63 When sensitive C-peptide measure ments are performed, 30–80% of people with long-term type 1 diabetes are found to be insulin microsecretors. 64 – 67 So, although endogenous β-cell quantity and function decline with longer disease duration, this decline does not progress to a complete loss of all β cells. 64 – 67 This finding is noteworthy because in the Diabetes Control and Complications Trial 68 , 69 persistent C-peptide secretion was associated with reduced development of retinopathy, nephropathy, and hypoglycaemia. Additionally, the persistence of C-peptide secretion in people with long-term type 1 diabetes could improve glucagon responses to hypoglycaemia. 70 Moreover, the presence of residual C-peptide secretion after the diagnosis of disease could also increase the possibility of an improved effect of interventions targeted at rescuing or augmenting the survival of this residual pool of β cells. Analyses of pancreatic specimens from the Network of Pancreatic Organ Donors repository have not found evidence of either increased neogenesis or proliferation in pancreatic cells from donors with type 1 diabetes. 63 Thus, the mechanisms underlying the persistence of residual β cells in people with long-term type 1 diabetes remain unclear. Identifying pathways that have allowed these cells to escape the autoimmune attack could yield insight into new therapeutic approaches.

β-cell abnormalities might also contribute to type 1 diabetes pathogenesis, leading to the notion of so-called β-cell suicide. β-cell HLA class I overexpression is common in pancreatic sections from cadaveric donors with type 1 diabetes. This overexpression serves as a homing signal for cytotoxic T lymphocytes. 49 However, whether this signal is a primary β-cell defect or a response to a stimulus (eg, a viral infection) is not yet known. Additionally, evidence also exists for increased β-cell endoplasmic reticulum stress linked with accelerated β-cell death. 71 , 72 Endoplasmic reticulum stress in β cells has also been associated with alterations in mRNA splicing and errors in protein translation and folding; the resultant protein products have been proposed as potential immunogenic neoantigens. 73

In addition to these defects in the β-cell compartment, alterations in non-endocrine islet cells and the exocrine pancreas have also been described ( figure 4 ). These defects include abnormalities in the islet extracellular matrix 74 , 75 and in islet innervation and vascularity. 76 – 78 Data have also placed a renewed emphasis on the role of exocrine pancreatic pathology in type 1 diabetes. Compared with healthy individuals, people with type 1 diabetes have a decreased pancreatic weight and volume that continues to decrease with disease duration. 79 , 80 This finding could be explained by developmental defects, or pancreatic atrophy in response to loss of the paracrine and pro-growth effects of insulin or chronic inflammation, or even autoimmune-mediated exocrine destruction. These possibilities are all topics of active investigation.

(A) Type 1 diabetes is characterised by a variety of abnormalities that involve both the islet and the exocrine pancreas. The hallmark of type 1 diabetes is loss of insulin-producing β cells and immune infiltration of islets. However, the presence of insulitis, even within an individual pancreas, can be highly variable. (B) Immunofluorescent image of an insulitic islet from a cadaveric donor with long-term type 1 diabetes. Insulin is shown in blue and CD8+ T cells surrounding the islet are shown in yellow. (C) Haematoxylin and eosin staining of an islet from a cadaveric donor that exhibits a classic pattern of insulitis. The islet is circled with a yellow dotted line. The infiltrating immune cells are circled in red and indicated by arrows. (D) Haematoxylin and eosin staining of an islet, circled in yellow dotted line, from a cadaveric donor with long-term type 1 diabetes without any discernible immune infiltrate. By contrast with the islet in (C), this islet has evidence of peri-islet fibrosis as shown circled in red and indicated by arrows. Images B–D courtesy of M Campbell-Thompson, University of Florida, Gainesville, FL, USA.

Management of clinical disease

Methods of managing type 1 diabetes continue to improve, and although progress is generally slow and incremental, occasionally it is punctuated by rapid change. One such moment of change happened in 1993 with the publication of the Diabetes Control and Complication Trial. 81 This trial and the follow-up observational Epidemiology of Diabetes Interventions and Complications trial convincingly showed that achieving and maintaining glucose concentrations as close to those seen in people without diabetes as possible leads to a reduction in microvascular and cardiovascular type 1 diabetes complications. 82

Although insulin remains the mainstay of therapy, new insulin analogues with varying onsets and durations of action are widely available. Optimal glycaemic control requires multiple-dose insulin regimens that mimic physiological insulin release, with basal insulin for overnight and between-meal control, plus bolus doses of rapid-acting insulin analogues to cover ingested carbohydrate loads and treat hyperglycaemia. Insulin can be taken by injection (with an insulin pen if available) or, preferably for many people, with an insulin pump. 83 Ultra-rapid inhaled insulin is also available, but little enthusiasm for this preparation exists because of its fixed dosing (four or eight unit increments only), issues with consistent delivery, cost, and the need for pulmonary function testing. 84 A faster-acting subcutaneously-administered insulin (via injection or infusion) has also recently become available for clinical use. Appropriate insulin use requires frequent dosing adjustments for ingested carbohydrates, physical activity, and illness or stress.

While pramlintide is the only non-insulin medication approved for improved glycaemic control in patients with type 1 diabetes, metformin, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, and sodium-glucose co-transporter-2 (SGLT2) inhibitors have also been used of-label; however, fewer than 5% of patients use these medications. 85 Metformin, an insulin sensitiser, is the most commonly prescribed drug for people with type 1 diabetes who have insulin resistance but it has not been shown to be effective in people younger than 18 years who are overweight or obese and have type 1 diabetes. 86 Use of SGLT2 inhibitors is restricted in part because of early reports of euglycaemic diabetic ketoacidosis in people with type 1 diabetes treated with these compounds. A 2018 meta-analysis of these inhibitors suggests they are safe, 87 but more data are needed.

Glucagon therapy is also poised to undergo a resurgence in management of type 1 diabetes. Although only an emergency kit has been commercially available up until now for cases of severe hypoglycaemia leading to seizure or loss of consciousness, nasal and stable liquid formulations are being developed. The nasal formulation will be available as a rapid rescue therapy only, 88 whereas the stable liquid formulation could also be used in small doses for exercise and in dual hormone (ie, insulin and glucagon) closed-loop systems. 89 , 90

In the past 13 years, continuous glucose monitoring (CGM) and intermittently viewed CGM devices for at-home patient use with minimally invasive devices have become available, which have similar accuracy to capillary blood glucose monitors. 91 Both CGM and intermittently viewed CGM allow examination of glucose concentration patterns over time and, although CGM devices still need periodic calibration, they obviate the need for frequent capillary blood glucose measurements. CGM is more sophisticated than intermittently viewed CGM because it can give the user a warning on the basis of absolute or projected glucose values. When CGM is incorporated into hybrid closed-loop insulin-pump systems that automatically regulate basal infusion rates, but that require manual delivery of meal boluses by trained wearers to cover estimated carbohydrate intakes, substantial improvements in glucose variability and overall glycaemic control are seen ( figure 5 ). 93 Combined use of automated insulin delivery and CGM offers the prospect of an artificial pancreas with little input from the user. The substantial advances that have been made in pump and sensor technology and the increase in the number of trials to test their efficacy show that partially or fully automated systems could become a reality.

Sensor glucose profiles from 124 people with type 1 diabetes, of which 30 were adolescents (14–21 years; A) and 94 were adults (22–75 years; B), before (during run-in phase) and during the study phase using the Medtronic MiniMed 670 g hybrid closed-loop system (Medtronic, Northridge CA, USA) under clinical trial conditions. Median and IQR of sensor glucose values are given as a green line and band for the run-in phase, and a pink line and band for the study phase, respectively. In the run-in phase, the hybrid closed-loop system was in manual mode, with participants making all treatment decisions except for the pump automatically suspending before senor glucose concentrations became too low. In the study phase, the hybrid closed-loop system was in auto mode. Participants had less variability in their blood glucose concentration during auto mode. Reproduced from Garg et al, 92 with permission from Mary Ann Liebert.

Guidelines from the American Diabetes Association, International Society for Pediatric and Adolescent Diabetes, and Candian Diabetes Association suggest a HbA 1c target of less than 53 mmol/mol (7·0%) for adults and less than 58 mmol/mol (7·5%) in paediatric patients with type 1 diabetes; 94 – 96 however, most individuals do not achieve these targets. Although setting more aggressive targets is associated with achieving lower HbA 1c , 97 these targets should be individualised on the basis of many factors including comorbidities, patient capability and attitude, and available care resources 98 —eg, even lower targets are often prescribed for pregnant women and women anticipating pregnancy than those prescribed to other patients. 99 Higher targets might be appropriate for people with hypoglycaemia unawareness, history of severe hypoglycaemia, advanced complications, and short life expectancy. For optimal outcomes, people with diabetes should be cared for by a multidisciplinary care team, including diabetes educators, nurse practitioners, nurses, nutritionists, physician assistants, exercise physiologists, social workers, and psychologists. To optimise glycaemic control, clinical care with skilled and structured patient education and training sessions should be provided—including information on insulin adjustments, carbohydrate counting, and optimal use of available technology. 100

People with type 1 diabetes also risk developing other autoimmune diseases, sometimes as part of a poly-glandular autoimmune syndrome. A study 101 from the Type 1 Diabetes Exchange clinic registry noted the prevalence of autoimmune disease was 27% in a population of over 25 000 people with type 1 diabetes with a mean age of 23 years. The most common autoimmune disease is autoimmune thyroiditis (ie, Hashimoto thyroid itis and Graves’ disease) followed by coeliac disease. Other associated conditions include collagen-vascular diseases (eg, rheumatoid arthritis and lupus), autoimmune gastritis or pernicious anaemia, vitiligo, and Addison’s disease. Guidelines for the care of people with diabetes include periodic screening for these diseases, particularly thyroid and coeliac diseases. 102

Complications of type 1 diabetes

The discovery of insulin in 1922 transformed type 1 diabetes from a terminal to a treatable disease. Despite the advances in care discussed previously, the disease continues to be associated with substantial medical, psychological, and financial burden. Hypoglycaemia and ketoacidosis are persistent potentially life-threatening complications. Severe hypoglycaemic events requiring treatment assistance from another person occur at rates of 16–20 per 100 person-years; hypoglycaemic events leading to loss of consciousness or seizure occur at a rate of 2–8 per 100 person-years. 103 – 105 Recurrent hypoglycaemia results in an increased likelihood of hypoglycaemia unawareness and subsequent severe hypoglycaemic events, since recurrent hypoglycaemia reduces the glucose concentration that triggers the counter-regulatory responses to return to euglycaemia. 106 Hypoglycaemia unawareness can improve with edu cation, support, and glucose targets that are aimed at avoiding biochemical hypoglycaemia, while maintaining overall metabolic control. 107

Hypoglycaemic events are associated with adverse effects on cognitive function, 108 , 109 and are associated with 4–10% of type 1 diabetes-related deaths. 110 – 112 Observational studies suggest poor diabetes control does not reduce the risk of severe hypoglycaemia. 113 Notably, rates of severe hypoglycaemic events have been decreasing over time 104 and with CGM and other advanced diabetes technologies HbA 1c can be lowered into the target range without increasing the risk of severe hypoglycaemia. 114 Treatment in hospital for diabetic ketoacidosis occurs at a rate of 1–10 per 100 patient-years in paediatric populations with established type 1 diabetes, and accounts for 13–19% of type 1 diabetes-related mortality. 105 , 110 , 111 Incidence of diabetic ketoacidosis is higher among women than among men, and among people with higher HbA 1c levels than other people with type 1 diabetes.

Microvascular complications of the disease manifest primarily as retinopathy, neuropathy, and nephropathy, but also can affect cognitive function, the heart, and other organs. Hyperglycaemia is the primary risk factor for microvascular disease, and reducing HbA 1c through intensive diabetes management, particularly early during disease, is associated with striking (about 70%) reductions in incidence and slower progression of microvascular disease. However, differences in HbA 1c do not fully explain the variation in the incidence of complications and the severity of disease between individuals. Variability in glucose concentrations (both during the day and longer term) and glycosylation rates also probably have a role in interindividual differences. 115 , 116 Type 1 diabetes during puberty also appears to accelerate the development of complications. 117

Macrovascular complications of type 1 diabetes include atherosclerosis and thrombosis in the heart, peripheral arteries, and brain. By contrast with microvascular complications, the risk of cardiovascular complications does not appear to be as attenuated by intensive blood sugar control. Diabetic nephropathy, whether manifesting as microalbuminuria, macroalbuminuria, or a reduced glomerular filtration rate progressively augments the overall risk of macrovascular complications. 118 Cardiovascular disease remains the major cause of premature morbidity and mortality, with data 119 , 120 suggesting an 8–13-year shorter life expectancy for people with type 1 diabetes than for healthy individuals.

People with diabetes might also have both chronic and acute neurocognitive changes that include decline in cognitive function with detrimental effects on psychomotor speed, cognitive flexibility, attention, and visual perception. 121 , 122 Although the pathophysiology of neurocognitive changes is poorly understood, their development has been linked with both microvascular and macrovascular changes and changes in brain structure, neuronal loss, and cerebral atrophy. 123 , 124 Risk factors include developing diabetes early in life, chronic hyperglycaemia, and repeated hypoglycaemia.

In the past 25 years, among people with type 1 diabetes the risks of microvascular and macrovascular compli-cations have substantially decreased and outcomes have improved. 125 , 126 These improvements have been largely driven by better glycaemic control and improved management of associated risk factors—eg, hypertension and hyperlipidaemia. Several studies 127 – 130 have identified additional non-glycaemic risk factors for the development of complications. Genetic studies have not yielded strong associations between specific gene variants and complication status. Low levels of education and income have been associated with high risks of both micro-vascular and macrovascular complications. 127 Sex also appears to modify risk, since women with type 1 diabetes have been shown to have higher rates of all-cause premature mortality and vascular events than do men with type 1 diabetes. 128 In the past 5 years, new technologies have been designed to attempt to better predict future risk and complications by combining risk factors into probability models. Two examples are the QDiabetes 129 and QRISK3 130 web calculators that were developed with a prospective general practice dataset of 803 044 people with diabetes (44 440 with type 1 diabetes). These calculators can be used to predict 10-year risk for microvascular and macrovascular complications. However, continued work is needed in this area to combine prediction models with disease-specific bio- markers and disease-modifying therapies that can prevent sequelae.

An additional noteworthy complication of type 1 diabetes is the patient-reported burden of adverse also their family, friends, and caregivers. 131 Fear of hypoglycaemia is a prevalent issue, particularly for the families of very young children with type 1 diabetes. 132 Furthermore, poor quality of life is predictive of subsequent poor glycaemic control. 133

Disease-modifying therapies

For over 30 years, most efforts to cure type 1 diabetes have focused on altering the immune system’s attack on β cells. This approach began with trials of ciclosporin, an immunosuppressant that was given to inhibit T-cell activation. Although ciclosporin was unable to induce a durable disease remission, insulin requirements of patients decreased during active treatment, generating enthusiasm that immune modulation could treat type 1 diabetes. 134 – 136 Subsequently, other strategies have been tested in both primary and secondary prevention paradigms. Most efforts have focused broadly on tolerance induction by use of antigens or modulation of T-lymphocyte, B-lymphocyte, and cytokine responses. Some primary prevention studies have also used dietary approaches. 137 , 138

Antigen-based trials have used various forms of glutamate decarboxylase (GAD) protein, which have shown mixed but mostly negative results. 139 – 141 The Diabetes Prevention Trial—Type 1, tested whether oral or parental insulin prevented the development of type 1 diabetes in people who were autoantibody positive. Neither approach reduced diabetes development, but subgroup analyses suggested a benefit of oral insulin in individuals with the highest titres of insulin auto-antibodies. 142 , 143 Based on this finding, the Type 1 Diabetes TrialNet Network completed a trial 144 of low-dose oral insulin in a second cohort of individuals who were autoantibody positive with similar insulin autoantibody profiles, but this trial was also negative. Negative results were also observed in another trial investigating intranasal insulin. 145

Personalised strategies for tolerance induction are now also being pursued. One study tested repeated intradermal doses of a specific proinsulin peptide fragment in people with the HLA DRB1*0401 genotype, 146 for whom this peptide was identified to be specifically immunogenic. Clinical trials at diagnosis have also tested approaches aimed at modulating T-cell and B-cell responses. Despite many attempts at immune intervention, only four categories of drugs have shown efficacy in preserving C-peptide secretion in recent onset type 1 diabetes in randomised placebocontrolled trials. These drugs include a monoclonal antibody against the B-cell CD20 receptor (rituximab), 147 monoclonal antibodies against the T-cell CD3 receptor (teplizumab 148 , 149 and otelixizumab 150 ), cytotoxic T-lymphocyte protein 4 (CTLA4)-immunoglobulin-mediated co-stimulatory blockade with abatacept, 151 and alefacept, 152 which is a fusion protein that binds CD2 and targets CD4+ and CD8+ effector memory T cells. Although the phase 2 trials of these drugs met their primary or secondary endpoints, defined as an improvement in the C-peptide area-under-the-curve response during a mixed meal tolerance test, no drug has yet been able to induce insulin independence or progressed to a positive phase 3 trial that was translatable into clinical care. This gap in translating results from trials into clinical practice could highlight the need for alternative strategies. Combinatorial approaches that modulate multiple aspects of the immune response could result in better efficacy. For example, low-dose anti-thymocyte globulin in combination with granulocyte colony-stimulating factor has shown early and sustained efficacy in pilot studies 153 , 154 and is being tested in a phase 2 study () in recent-onset type 1 diabetes. Another approach is to intervene earlier in the disease process, at a time when greater β-cell mass remains. To this end, abatacept () and teplizumab () are being tested in stage 1 and stage 2 type 1 diabetes through the TrialNet Network. Even modest preservation of β-cell function could have long-term benefits, and better glycaemic control early in the disease course could mitigate the likelihood of complications. 155 – 157

One potential future therapy for type 1 diabetes is with replacement of β cells from an external source. Pancreas transplants have been performed for over 50 years and have become a standard-of-care treatment in individuals who have developed end-stage renal failure and require kidney transplantation. 158 Simultaneous kidney and pancreas transplantation in experienced centres can offer an up to 80% chance of insulin independence for over 5 years, but there is substantial surgical risk, and the requirement of immunosuppression. 159 Islet transplantation is a low-risk procedure, with donor islets infused into the liver via the portal vein. Shapiro and colleagues’ landmark work, by use of a steroid free Edmonton Protocol, 160 showed that islet transplantation could achieve insulin independence and offered an example of a successful and low-risk cell-based therapy. However, only a minority of islet transplant recipients achieve durable insulin independence. Moreover, morbidity associated with immunosuppression and limitations in the supply of donor islets restricts the number of people who can benefit from islet transplantation. 161 Currently, islet transplantation is used in a small subset of patients who have extremely severe hypoglycaemic unawareness. Even if insulin independence is not achieved, severe life-threatening hypoglycaemia can be prevented with minimal islet transplant function. 162 , 163

Cell therapy as a potential cure for type 1 diabetes remains a field of great interest. 2 Considerable effort has been focused on protocols to generate functional and glucose-responsive β cells from human embryonic stem cells or induced pluripotent stem cells from living donors. This approach offers the possibility of a limitless source of β cells that could be delivered in a semipermeable device that would permit functional insulin secretion but avoid the need for immuno-suppression. 164 Several small molecules, growth factors, hormones, and nutrients have been shown to promote modest β-cell neogenesis and proliferation. However, most positive results come from animal models and have been difficult to replicate in human studies. While stem-cell-based therapies and neogenesis are a source of hope for potential cures, they are not realistic treatments in the immediate future. 2

Other novel approaches include autologous haemopoietic stem-cell transplantation 165 , 166 and autologous T-regulatory cell administration. 167 – 169 In response to growing evidence highlighting an active role for the β cell in disease pathogenesis, several ongoing trials are testing drugs that have successfully targeted β-cell stress responses in mouse models of diabetes. 170

Conclusions

Over the past 50 years, people with type 1 diabetes and their medical-care providers have been tantalised with optimism and subsequently disappointed at the seemingly unobtainable cure on the horizon. However, this long journey has been punctuated by several pivotal successes, including the discovery of insulin in 1922, the first pancreatic transplantation in 1966, 171 the first insulin-pump studies, the first immunomodulatory trial in 1986, 136 and the first definitive evidence linking glycaemic control with complication status in 1993. 81 The past 25 years has brought an upsurge of technological advances, including designer insulin analogues, smart insulin pumps, continuous glucose sensors, and closed-loop insulin delivery systems.

Clinicians, investigators, and patients have gained a better appreciation of the true complexity of type 1 diabetes, and humility in the face of many unsuccessful trials aimed at inducing a durable disease remission. While scientists continue to untangle the complicated pathogenesis of the disease, patients and health-care providers should focus on advocating for improved access to modern advances in diabetes care, especially for affordable insulin analogues and technologies that can reduce the burden of managing this chronic disease. When insulin was discovered, the University of Toronto freely licensed the right to manufacture the drug; yet, people in resource-limited environments continue to die because they have no access to insulin. 172

Additionally, crucial research must continue into strategies to prevent disease onset and preserve or restore β-cell function. These approaches offer the promise of ameliorating or eliminating disease complications, and greatly improving outcomes for those who have the disease. Continued development of new low-cost, low-burden, and highly effective therapies to improve glycaemic control is also needed. These approaches could include investigation into the effects of different dietary composition on glycaemic outcomes, and the safety and efficacy of open-source patient-designed artificial pancreas innovations. Given observed differences in care, health-care providers must be committed to initiatives for continuous quality improvement, with a focus on increasing uptake and implementation of best standards of care. A greater focus on patient-centred outcomes has been present in trials, and further exploration of these important endpoints is also crucial. If stakeholders in the field concentrate on the areas that are most likely to have a long-term effect, management of type 1 diabetes is poised to undergo further radical transformation.

Search strategy and selection criteria

We searched MEDLINE for publications in English published between Jan 1, 2014, and March 1, 2018, using the term “type 1 diabetes” and MEDLINE subheadings and selected papers on the basis of our opinion of their scientific importance. Research published since the 2014 Lancet Seminar on this topic was given particular attention. We provide an overview of type 1 diabetes focusing on updating the reader on recent advances and controversies.

Acknowledgments

This work was partly supported by grants from the National Institutes of Health, JDRF, the Veteran’s Administration, Diabetes UK, the Leona M and Harry B Helmsley Charitable Trust, the BIRAX Regenerative Medicine Initiative, the Ball Brothers Foundation, George and Francis Ball Foundation, Sigma Beta Sorority, Cryptic Masons Medical Research Foundation, and the Luke Weise Research Fund. We thank W Tamborlane, C Matthews, and J Kushner for their review of a draft of this Seminar. We thank M Campbell-Thompson, F Syed, and T Weinzerl for assistance with figures. And we thank T Lewallen and M Wales for administrative support.

Declaration of interests

LAD reports personal fees from Eli Lilly, and grants from Medtronic, Sanofi, Xeris, Caladrius, Dexcom, and Janssen outside the submitted work. RAO holds a UK Medical Research Council institutional Confidence in Concept grant to develop a 10 SNP biochip type 1 diabetes genetic test in collaboration with Randox. CE-M declares no competing interests.

Contributor Information

Linda A DiMeglio, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA;

Carmella Evans-Molina, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA;

Richard A Oram, Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.