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Psychological and clinical challenges in the management of type 1 diabetes during adolescence: a narrative review.

1. Introduction

2. psychological issues in adolescents with t1d, 3. factors influencing glycemic control in adolescents with t1d, 3.1. dietary habits, 3.2. physical fitness, 3.3. sleep characteristics, 3.4. risky behaviors and substance abuse, 3.5. mental health, 3.6. social and school environment, 4. strategies to improve diabetes management in adolescents with t1d, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Bombaci, B.; Torre, A.; Longo, A.; Pecoraro, M.; Papa, M.; Sorrenti, L.; La Rocca, M.; Lombardo, F.; Salzano, G. Psychological and Clinical Challenges in the Management of Type 1 Diabetes during Adolescence: A Narrative Review. Children 2024 , 11 , 1085. https://doi.org/10.3390/children11091085

Bombaci B, Torre A, Longo A, Pecoraro M, Papa M, Sorrenti L, La Rocca M, Lombardo F, Salzano G. Psychological and Clinical Challenges in the Management of Type 1 Diabetes during Adolescence: A Narrative Review. Children . 2024; 11(9):1085. https://doi.org/10.3390/children11091085

Bombaci, Bruno, Arianna Torre, Alessandro Longo, Maria Pecoraro, Mattia Papa, Lacrima Sorrenti, Mariarosaria La Rocca, Fortunato Lombardo, and Giuseppina Salzano. 2024. "Psychological and Clinical Challenges in the Management of Type 1 Diabetes during Adolescence: A Narrative Review" Children 11, no. 9: 1085. https://doi.org/10.3390/children11091085

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Knowledge of young people living with type 1 diabetes and their caregivers about its management

Affiliations.

• 1 Department of Population and Health, University of Cape Coast, Cape Coast, Ghana.
• 2 Cape Coast Teaching Hospital, Central Region, Cape Coast, Ghana.
• 3 Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, Kings College London, London, UK.
• PMID: 36448367
• PMCID: PMC10006669
• DOI: 10.1002/nop2.1498

Aims and objective: We sought to investigate knowledge and skills of type 1 diabetes (T1D) management among young people living with the disease and their caregivers. Our aim is to provide baseline evidence to inform T1D self-management education for young people living with the disease and their caregivers.

Background: Both local and international guidelines recommend ongoing T1D self-management education for people living with the disease. This is because T1D often develops among young people who rarely have the competencies to adequately manage their condition. However, the extent to which young people living with T1D and their caregivers can self-manage this chronic disease in a low-resource country like Ghana is unknown.

Methods: Using a phenomenological study design, semi-structured interviews were conducted with 28 young people living with type 1 diabetes, 12 caregivers and 6 healthcare providers in southern Ghana. Data were collected at homes, hospitals and support group centres of participants via face-to-face interviews, photovoice and video-conferencing. The data were analysed thematically using QSR NVivo 11.

Results: The young people living with T1D and their caregivers demonstrated knowledge and skills in the self-monitoring of blood glucose, and the treatment of hyperglycaemia. Areas of more marginal or lack of knowledge were concerning carbohydrate counting, severe hypoglycaemia and the management of intercurrent illnesses. Young persons living with T1D and their caregivers received their management information from healthcare and non-healthcare providers. Access to diabetes self-management education influenced T1D management knowledge and practices.

Conclusion: Young people living with type 1 diabetes and their caregivers possessed limited scope of knowledge on type 1 diabetes self-management. Multiple sources of T1D knowledge were found, some of which may not be helpful to patients. The knowledge gaps identified compromises transitional independence and self-management capacity.

Relevance to clinical practice: It is important for clinicians and organizations that provide T1D education to provide diabetes self-management education also on managing hypoglycaemia, carbohydrate counting and managing T1D during intercurrent life events among young people living with T1D.

No patient or public contribution: Patients and their caregivers were interviewed as research participants. They did not conceptualize, analyse, interpret or prepare the manuscript.

Keywords: Covid-19; diabetes mellitus; diabetes self-management education; health literacy; juvenile diabetes; knowledge; nutrition; self-care; vignette.

© 2022 The Authors. Nursing Open published by John Wiley & Sons Ltd.

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Conflict of interest statement

The authors declare no conflict of interest.

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Understanding adult-onset type 1 diabetes, article information, adult-onset type 1 diabetes: current understanding and challenges.

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R. David Leslie , Carmella Evans-Molina , Jacquelyn Freund-Brown , Raffaella Buzzetti , Dana Dabelea , Kathleen M. Gillespie , Robin Goland , Angus G. Jones , Mark Kacher , Lawrence S. Phillips , Olov Rolandsson , Jana L. Wardian , Jessica L. Dunne; Adult-Onset Type 1 Diabetes: Current Understanding and Challenges. Diabetes Care 1 November 2021; 44 (11): 2449–2456. https://doi.org/10.2337/dc21-0770

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

Clinically, it has been relatively easy to distinguish the acute, potentially lethal, childhood-onset diabetes from the less aggressive condition that affects adults. However, experience has taught us that not all children with diabetes are insulin dependent and not all adults are non–insulin dependent. Immune, genetic, and metabolic analysis of these two, apparently distinct, forms of diabetes revealed inconsistencies, such that insulin-dependent and immune-mediated diabetes was redefined as type 1 diabetes, while most other forms were relabeled as type 2 diabetes. Recent data suggest a further shift in our thinking, with the recognition that more than half of all new cases of type 1 diabetes occur in adults. However, many adults may not require insulin at diagnosis of type 1 diabetes and have a more gradual onset of hyperglycemia, often leading to misclassification and inappropriate care. Indeed, misdiagnosis occurs in nearly 40% of adults with new type 1 diabetes, with the risk of error increasing with age ( 1 , 2 ). To consider this important issue, JDRF convened a workshop of international experts in November 2019 in New York, NY. In this Perspective, based on that workshop, we outline the evidence for a new viewpoint, suggesting future directions of research and ways to alter disease management to help adults living with type 1 diabetes.

Incidence of Type 1 Diabetes Among Adults Worldwide

Adult-onset type 1 diabetes is more common than childhood-onset type 1 diabetes, as shown from epidemiological data from both high-risk areas such as Northern Europe and low-risk areas such as China ( 3 – 8 ). In southeastern Sweden, the disease incidence among individuals aged 0–19 years is similar to that among individuals 40–100 years of age (37.8 per 100,000 persons per year and 34.0/100,000/year, respectively) ( 3 ). Given that the comparable incidence spans only two decades in children, it follows that adult-onset type 1 diabetes is more prevalent. Similarly, analysis of U.S. data from commercially insured individuals demonstrated an overall lower incidence in individuals 20–64 years of age (18.6/100,000/year) than in youth aged 0–19 years (34.3/100,000/year), but the total number of new cases in adults over a 14-year period was 19,174 compared with 13,302 in youth ( 4 ). Despite the incidence of childhood-onset type 1 diabetes in China being among the lowest in the world, prevalence data show similar trends across the life span. From 2010–2013, the incidence was 1.93/100,000 among individuals aged 0–14 years and 1.28/100,000 among those 15–29 years of age versus 0.69/100,000 among older adults ( 5 ). In aggregate, adults comprised 65.3% of all clinically defined newly diagnosed type 1 diabetes cases in China, which is similar to estimates using genetically stratified data from the population-based UK Biobank using a childhood-onset polygenic genetic risk score (GRS) ( 6 ). It is important to note that the proportion would likely be higher if autoimmune cases not requiring insulin initially were classified as type 1 diabetes. For example, in a clinic-based European study, the proportion of adults with diabetes not initially requiring insulin yet with type 1 diabetes–associated autoantibodies was even higher than those started on insulin at diagnosis with a defined type 1 diabetes diagnosis ( 9 ). Moreover, in an adult population-based study in China, the fraction (8.6%) with diabetes not requiring insulin yet with type 1 diabetes–associated autoantibodies was similar to that in Europe, implying that there could be over 6 million Chinese with adult-onset type 1 diabetes ( 10 ). While there is a wide range in the incidence of type 1 diabetes across different ethnic groups, even using differing methods of case identification ( 7 ), these data support the notion that, worldwide, over half of all new-onset type 1 diabetes cases occur in adults.

Natural History Studies of Type 1 Diabetes

Our understanding of the natural history of type 1 diabetes has been informed by a number of longitudinal and cross-sectional studies. At one end of the spectrum are prospective birth cohort studies, such as the BABYDIAB study in Germany and The Environmental Determinants of Diabetes in the Young (TEDDY) study, which includes sites in Germany, Finland, Sweden, and the U.S. While these studies now have the potential to explore the pathogenesis of islet autoimmunity by being extended into adulthood, they have primarily focused on events occurring in childhood ( 11 ). Clinical centers in North America, Europe, and Australia collaborate within Type 1 Diabetes TrialNet, a study that identifies autoantibody-positive adults and children in a cross-sectional manner to examine the pathogenesis of type 1 diabetes and to perform clinical trials on those at high risk in order to preserve β-cell function ( 12 ). At the other end of the spectrum, the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study is a case-cohort study nested in the U.K. prospective adult population-based EPIC study ( 13 ), while the clinical, immunogenetic, and metabolic characteristics of autoimmune adult-onset type 1 diabetes have been extensively studied in large American, European, and Chinese studies, including UK Prospective Diabetes Study (UKPDS), Action LADA, Scandia, Non Insulin Requiring Autoimmune Diabetes (NIRAD), and LADA China ( 9 , 14 – 19 ). Based on these cross-sectional and prospective studies, considerable data have been generated to define differences within type 1 diabetes according to the age at onset. Here, we highlight key aspects of age-related genetic, immune, and metabolic heterogeneity in type 1 diabetes. Of note, the term latent autoimmune diabetes in adults (LADA) has been used to describe adults with slowly progressive autoimmunity, sometimes exhibiting features overlapping with those of type 2 diabetes ( 9 , 14 , 18 ). At the outset of the workshop and for the purposes of this Perspective, LADA was not considered a unique entity; rather, we considered the classification of type 1 diabetes to include all individuals with evidence of autoimmunity, regardless of the trajectory of disease development (i.e., rapid or slowly progressive) or other associated demographic and/or clinical features (e.g., obesity).

Age-Related Genetic Heterogeneity

Type 1 diabetes shows heterogeneity across a broad range of clinical, genetic, immune, histological, and metabolic features ( 20 ). Childhood-onset type 1 diabetes is most often attributed to susceptibility alleles in human leukocyte antigen (HLA), which contribute ∼50% of the disease heritability. Whereas ethnic differences exist, notably for specific HLA genotypes, several broad principles apply. Compared with childhood-onset disease, adult-onset type 1 diabetes cases show lower type 1 diabetes concordance rates in twins ( 21 ), less high-risk HLA heterozygosity ( 19 ), lower HLA class I ( 14 ), more protective genotypes ( 14 , 15 ), and lower GRS ( 6 , 22 ), which are calculated by summing the odds ratios (OR) for disease-risk alleles.

Diabetes-Associated Immune Changes

Adult-onset type 1 diabetes, like childhood-onset type 1 diabetes, is associated with the presence of serum autoantibodies against β-cell antigens. Serum glutamic acid decarboxylase (GADA) autoantibodies may be useful as a predictor of type 1 diabetes in adults, as adult-onset cases most often present with GADA positivity ( 9 , 10 , 15 , 17 , 18 , 20 , 22 ) and possess an HLA-DR3 genotype ( 9 , 14 , 15 , 20 , 21 , 23 ). In one prospective study of a general population, the hazard risk of incident diabetes in those with a high type 1 diabetes GRS and GADA positivity was 3.23 compared with all other individuals, suggesting that 1.8% of incident diabetes in adults was attributable to that combination of risk factors ( 13 ). In adult-onset type 1 diabetes, multiple diabetes-associated autoantibodies tend to be less prevalent with increasing age at diagnosis ( 1 , 8 ), yet GADA remains the dominant autoantibody irrespective of the need for insulin treatment at diagnosis and irrespective of ethnicity ( 9 , 17 , 18 , 24 , 25 ), even despite a paucity of HLA DR3, as in Japan and China ( 17 , 18 ). In contrast, childhood-onset type 1 diabetes cases often have insulin autoantibodies and an HLA-DR4 genotype, higher identical twin disease concordance, more HLA heterozygosity, and higher GRS ( 20 ). Taken together, these data indicate that type 1 diabetes is heterogeneous across the spectrum of diagnoses, suggesting that pathogenesis and optimal therapy are also diverse.

Data from the TrialNet Pathway to Prevention cohort demonstrated lower risk of progression to type 1 diabetes in adults than children, even when both show multiple autoantibodies on a single occasion and are monitored over 10 years ( 12 ). One recent analysis found that the 5-year rate of progression to diabetes in multiple autoantibody–positive adults was only ∼15%, with a number of them remaining diabetes-free for decades ( 26 ). A combined cohort study, known as the Slow or Nonprogressive Autoimmunity to the Islets of Langerhans (SNAIL) study, is following such “slow progressors” with multiple autoantibodies who have yet to progress to stage 3 type 1 diabetes (i.e., clinical diagnosis) over at least a 10-year period ( 27 ). Many of these slow progressors lose disease-associated autoantibodies over time, adding complexity to cross-sectional classification ( 28 ). Based on estimates from natural history studies, slow progressors, even if identified when young, cannot account for all autoimmune adult-onset diabetes, indicating that autoantibodies must develop at all ages ( 11 ). However, little is known about those who initially develop autoimmunity as adults, mostly due to the lack of longitudinal studies focusing on this population.

People with type 1 diabetes, in contrast to the majority of those with type 2 diabetes, have altered adaptive immunity (i.e., islet autoantibodies and T-cell activation), while innate immune changes, including cytokine changes, are common to both ( 29 ). Increased T-cell activation by islet proteins has also been found in a proportion of adults with initially non-insulin-requiring diabetes, even when they lack diabetes autoantibodies ( 30 ). However, there is a paucity of immune studies on adult-onset type 1 diabetes and few histologic studies. An analysis of tissues from the Network for Pancreatic Organ Donors with Diabetes (nPOD) showed no relationship between age at diabetes onset and the frequency of islet insulitis ( 31 ). The composition of islet insulitis differs in very young children compared with older individuals, with the former having an increased frequency of B cells in islet infiltrates ( 32 ). However, relating pancreatic histological changes to changes in peripheral blood remains a challenge.

Adults with new-onset type 1 diabetes are at increased risk of other autoimmune conditions. About 30% of individuals with adult-onset type 1 diabetes have thyroid autoimmunity ( 27 , 29 ). In addition, adults with type 1 diabetes who possess high-titer GADA and/or multiple islet autoantibodies are at increased risk of progression to hypothyroidism ( 24 , 33 ). In a large population-based Chinese study, the prevalence of adult-onset type 1 diabetes was 6% among initially non-insulin-requiring diabetes cases, and 16.3% of them had thyroid autoimmunity (OR 2.4) ( 10 ). Of note, those with islet antigen 2 autoantibodies had a high risk of tissue transglutaminase autoantibodies, a marker for celiac disease (OR 19.1) ( 10 ). Thus, in the clinical setting, there should be a high index of suspicion for other autoimmune conditions in individuals with adult-onset type diabetes, and associated autoimmunity should be screened where clinically indicated.

Metabolic Characteristics of Adult-Onset Type 1 Diabetes

Age-related differences in type 1 diabetes extend to metabolic parameters. C-peptide at diagnosis is higher in adults than children, driven in part by higher BMI ( 34 ). Analysis of U.K., TrialNet, and Chinese cohorts has identified two distinct phases of C-peptide decline in stage 3 disease: an initial exponential fall followed by a period of relative stability. Along with initial differences at the time of clinical diagnosis, the rate of decline over 2–4 years was inversely related to age at onset ( 10 , 34 – 36 ). Furthermore, the U.S. T1D Exchange Study found that glycemic control was better in adults with type 1 diabetes than in children and adolescents with type 1 diabetes ( 37 ). The American Diabetes Association (ADA) targets for glycemia are higher in children, so that in this same cohort, 17% of children, compared with 21% of adults, achieved the ADA hemoglobin A 1c (HbA 1c ) goal of <7.5% and <7.0%, respectively ( 37 ). Other factors confound this relationship between age at diagnosis and metabolic control. First, individuals with adult-onset type 1 diabetes are more likely to have residual insulin-producing β-cells and persistent measurable C-peptide in disease of long duration, the latter of which has been linked to improved glycemic control ( 38 , 39 ). Second, individuals with adult-onset type 1 diabetes, initially not on insulin therapy, tend to have worse metabolic control than people with type 2 diabetes, even when receiving insulin treatment ( 9 , 40 ). The sole exception is the LADA China study, where worse control was noted only among those with a high GAD titer ( 18 ). Metabolic differences between adults and children extend beyond C-peptide. Adults with autoantibody positivity who progressed to type 1 diabetes were less likely than very young children to exhibit elevated proinsulin/C-peptide ratios prior to stage 3 disease onset ( 41 ). In addition, in individuals with disease of long duration, those diagnosed at an older age had evidence of improved proinsulin processing and nutrient-induced proinsulin secretory capacity ( 42 ).

Diagnosis and Management of Adult-Onset Type 1 Diabetes

Correctly identifying diabetes etiology and type is difficult, and misclassification may occur in up to 40% of adults presenting with type 1 diabetes ( 1 , 2 ). Reasons underlying misclassification are multiple and include 1 ) lack of awareness that the onset of type 1 diabetes is not limited to children; 2 ) the overwhelming majority of people developing diabetes as older adults have type 2 diabetes, contributing to a confirmation bias ( 2 ); 3 ) typical clinical criteria, such as BMI and metabolic syndrome, can be poor discriminators, especially as rates of obesity in the overall population increase ( 9 , 43 ); 4 ) clinical characteristics of adult-onset type 1 diabetes can masquerade as type 2 diabetes, given their slow metabolic progression and risk of metabolic syndrome (which occurs in about 40%), so that the distinction between types of diabetes may be blurred ( 43 – 45 ); and 5 ) lack of awareness of and accessibility to biomarkers that may serve as tools to distinguish type 1 diabetes and type 2 diabetes.

Tools to distinguish type 1 and type 2 diabetes are under active development. For example, classification models integrating up to five prespecified predictor variables, including clinical features (age of diagnosis and BMI) and clinical biomarkers (autoantibodies and GRS) in a White European population, had high accuracy to identify adults with recently diagnosed diabetes with rapid insulin requirement despite using GRS derived from childhood-onset type 1 diabetes. While GRS have the potential to assist diagnosis of type 1 diabetes in uncertain cases, they are not yet widely available in clinical practice. Moreover, it is important to note that while the model was optimized with the inclusion of all five variables, the addition of GRS had only a modest effect on overall model performance ( 22 ).

Classification can be aided by the measurement of autoantibodies and C-peptide. Recommended autoantibodies to assay at the time of diagnosis include those to insulin (insulin autoantibody), glutamate decarboxylase isoform 65 (GAD65A), insulinoma antigen 2, and zinc transporter isoform 8 (Znt8A), with GAD65A being the most prevalent autoantibody among adults. High levels or the presence of more than one antibody increases the likelihood of type 1 diabetes. However, it is important to realize that islet autoantibodies are a continuous marker that can also occur in the population without diabetes. As with many other tests, an abnormal test is usually based on a threshold signal from control populations without diabetes, usually the 97.5th or the 99th centile. Therefore, false-positive results with these assays can occur and can be reduced by using higher-specificity assays or thresholds and targeting testing toward those with clinical features suggestive of type 1 diabetes ( 46 ). Finally, since antibody levels can wane over time in established type 1 diabetes, the absence of autoantibodies does not rule out the possibility of a diagnosis of type 1 diabetes.

Measurement of C-peptide, paired with a blood glucose in the same sample, provides an estimate of endogenous insulin production and has the most utility in disease of long duration when levels fall below 300 pmol/L ( 39 , 47 ). However, C-peptide levels are typically higher at presentation and may be difficult to distinguish from levels in type 2 diabetes, which are usually >600 pmol/L. Thus, thresholds of C-peptide that clearly delineate type 1 diabetes from type 2 diabetes at diagnosis cannot be categorically defined, and C-peptide must be interpreted within the context of other clinical and laboratory features. Measurement of a random nonfasting C-peptide is superior to fasting C-peptide in identifying type 1 diabetes ( 48 ) and is well correlated with stimulated C-peptide levels measured during a mixed-meal tolerance test, which is considered the gold standard assessment of insulin secretory function in established type 1 diabetes ( 49 ). A recent analysis found that concomitant blood glucose ≥144 mg/dL (8 mmol/L) increased the specificity of random C-peptide in predicting a stimulated C-peptide level <600pmol/L, suggesting this is a reasonable threshold of blood glucose to employ for C-peptide interpretation ( 49 ).

C-peptide also can be used to guide therapy ( 50 ). Individuals with a random C-peptide level ≤300 pmol/L should be managed mainly with insulin. For those with random C-peptide levels >300 pmol/L, insulin could be combined with other diabetes therapies, although evidence about safety and efficacy is limited. It is generally agreed that sulfonylureas should be avoided because of the potential to hasten β-cell failure ( 50 ). There is concern for increased risk of diabetic ketoacidosis (DKA) with sodium–glucose cotransporter 1 (SGLT1) and SGLT2 inhibitors when these agents are used in type 1 diabetes, especially in nonobese individuals who may need only low dosages of insulin ( 51 ). All other agents could be considered for therapy in those not requiring insulin initially. In individuals with random C-peptide levels exceeding 600 pmol/L, management can be much as recommended for type 2 diabetes, with the caveats outlined above ( 50 ). An important consideration is that loss of β-cell function may be rapid in autoimmune diabetes. As such, individuals treated without insulin should be closely monitored.

In the absence of prospectively validated decision support tools that have been tested in multiethnic populations, we suggest, as an approach to aid the practicing physician, assessment of age, autoimmunity, body habitus/BMI, background, control, and comorbidities, using the acronym AABBCC ( Table 2 ). This approach includes the clinical consideration of autoimmunity and other clinical features suggestive of type 1 diabetes, including age at diagnosis, low BMI, an unexplained or rapid worsening of clinical course manifesting as a lack of response or rising HbA 1c with type 2 diabetes medications, and a rapid requirement for insulin therapy, especially within 3 years of diagnosis. It should be emphasized that among these features, age at diagnosis (<40 years), low BMI (<25 kg/m 2 ), and rapid need for insulin therapy are the most discriminatory ( 43 ). We recommend measurement of islet antibodies and C-peptide be considered in all older people with clinical features that suggest type 1 diabetes, with islet autoantibodies being the initial test of choice in short-duration disease (<3 years) and C-peptide the test of choice at longer durations.

Diabetes-Associated Comorbidities and Complications

The U.S. SEARCH for Diabetes in Youth study reported that nearly 30% of youth with newly diagnosed type 1 diabetes age <20 years presented with DKA ( 52 ). The frequency of DKA among adults at diagnosis with type 1 diabetes is unknown but is believed to be lower given that they often have higher C-peptide levels at diagnosis and a slower decline in β-cell function over time, even in those requiring insulin initially ( 34 ). Among childhood-onset type 1 diabetes, most episodes of DKA beyond diagnosis are associated with insulin omission, pump failure, or treatment error ( 53 ). However, for adults with type 1 diabetes, the primary risk factors are noncompliance and infections ( 54 ), the former sometimes due to the cost of insulin ( 55 ). Thus, there is a need to further understand DKA in adults, not least because it is associated with long-term worsening glycemic control ( 56 ).

Hypoglycemia

Fear of hypoglycemia remains a major problem in the clinical management of adults with type 1 diabetes ( 57 ), influencing quality of life and glycemic control. The effect of diabetes duration or age at diagnosis on hypoglycemia risk is not consistent among different studies. However, α-cell responses to hypoglycemia and hypoglycemia risk are both lower in individuals with higher C-peptide levels ( 38 ). Because residual C-peptide is more likely to be observed in those with a later age of onset, hypoglycemia risk may be different between those with childhood- and adult-onset diabetes. While insulin pumps and continuous glucose monitors are associated with improved glycemic control and reduced hypoglycemia ( 37 ), adults may show reluctance or inertia in adopting newer technologies. In the T1D Exchange study population, 63% of adults used an insulin pump while only 30% used a continuous glucose monitor, and use of these technologies tended to be lower in adults than in children ( 37 ). Factors that dictate use of these technologies are multiple and may include reduced access to or acceptance of wearable technology, challenges with insurance coverage, especially in the context of past misclassification, and/or inadequate education about hypoglycemia risk ( 58 ). A better understanding of potential barriers to technology use in adult-onset type 1 diabetes is needed. Furthermore, little is known about changes in hypoglycemia risk across the life span of individuals with adult-onset disease, representing an important gap in knowledge.

Microvascular and Macrovascular Disease Complications

Despite the prevalence of adult-onset type 1 diabetes, there is a paucity of data on the burden of microvascular complications in this population. Current knowledge is largely based on small, cross-sectional studies. In aggregate, these studies suggest that the prevalence of nephropathy and retinopathy are lower in adult-onset type 1 than in type 2 diabetes, but this conclusion is potentially confounded by diabetes duration. For example, the prevalence of nephropathy and retinopathy was lower in Chinese individuals with adult-onset type 1 diabetes than in those with type 2, but only in those with a disease duration <5 years, while in the Botnia Study, retinopathy risk in adult-onset type 1 diabetes increased, as expected, with disease duration ( 59 ). Two substantial prospective studies recently reported that those adults with diabetes enrolled in the UKPDS who were also GADA positive (i.e., presumably with type 1 diabetes) compared with those who were GADA negative (with type 2 diabetes) showed a higher prevalence of retinopathy and lower prevalence of cardiovascular events ( 60 , 61 ). These results are consistent with people with adult-onset type 1 diabetes compared with those with type 2 diabetes, showing a general tendency to higher HbA 1c levels ( 40 , 44 , 60 , 61 ) as well as reduced traditional cardiovascular risk factors, including reduced adiposity (BMI and waist circumference), metabolic (lipid levels), and vascular (blood pressure) profiles ( 9 , 24 , 62 ). Nevertheless, all-cause mortality and cardiovascular mortality rates in such individuals with adult-onset type 1 diabetes ( 59 ) are still higher than those among individuals without diabetes. In addition, there are discrepancies across studies, likely related to differences in populations under study (i.e., age, race/ethnicity, and diabetes duration), lack of consistent case definitions (i.e., adult-onset type 1 diabetes or LADA cases), and different outcomes, as well as small sample sizes with insufficient events on which to base strong recommendations.

Psychosocial Challenges

Negative stressors, including pressure to achieve target HbA 1c levels, lifestyle considerations, and fear of complications, are factors leading to the increased frequency of mood disorders, attempted suicide, and psychiatric care in adults with diabetes ( 63 ). In individuals who have experienced misclassification, additional stress derives from conflicting messages about the nature of their diabetes. Among adults with type 1 diabetes, those with high psychological coping skills (e.g., self-efficacy, self-esteem, and optimism) and adaptive skills may buffer the negative effect of stress and should be cultivated ( 64 ). Relationship challenges, including sexual intimacy, starting a family, caring for children, and relational stress, are major stressors for adults with type 1 diabetes ( 65 ). In addition, there is the looming threat of complications, including blindness and amputations ( 65 ). Adults with type 1 diabetes describe a sense of powerlessness, fear of hypoglycemia, and the challenges of both self-management and appropriate food management ( 66 ). A common misunderstanding is that while they face the same life choices associated with type 2 diabetes (e.g., weight loss, exercise, and limiting intake of simple sugars), adults with type 1 diabetes may require different management skills ( 67 ). Moreover, there is a strong association in adults with type 1 diabetes between chronic, stressful life events and fluctuating HbA 1c , possibly due to indirect mechanisms, including adherence to diabetes management ( 68 ). Whether these risks differ between those diagnosed as children or as adults is unclear and requires additional study.

In this Perspective, we have summarized the current understanding of adult-onset type 1 diabetes while identifying many knowledge gaps ( Table 1 ). Epidemiological data from diverse ethnic groups show that adult-onset type 1 diabetes is often more prevalent than childhood-onset type 1 diabetes. However, our understanding of type 1 diabetes presenting in adults is limited. This striking shortfall in knowledge ( Table 1 ) results in frequent misclassification, which may negatively impact disease management. Here, we outline a roadmap for addressing these deficiencies ( Fig. 1 ). A cornerstone of this roadmap is a renewed emphasis on the careful consideration of the underlying etiology of diabetes in every adult presenting with diabetes.

Proposed roadmap to better understand, diagnose, and care for adults with type 1 diabetes (T1D). Created in BioRender ( BioRender.com ).

Knowledge gaps

In the absence of data-driven classification tools capable of estimating individual-level risk, we offer a simple set of questions, incorporating what we have termed the AABBCCs of diabetes classification and management ( Table 2 ). In parallel, we invite the research community to join together in addressing key gaps in knowledge through studies aimed at defining the genetic, immunologic, and metabolic phenotype of adult-onset type 1 diabetes with the goal of using this knowledge to develop improved approaches for disease management and prevention ( Fig. 1 ).

AABBCC approach to diabetes classification

Acknowledgments. Sharon Saydah, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, attended the workshop and participated in subsequent discussions of the manuscript. Elizabeth Seaquist, Division of Diabetes, Endocrinology, and Metabolism at the University of Minnesota, participated in the workshop. The authors acknowledge Marilyn L. Wales for her assistance with formatting the manuscript.

Funding and Duality of Interest. This manuscript is the result of a one-day meeting held at JDRF headquarters in New York, NY. Financial support for the workshop was provided by JDRF and Janssen Research and Development, LLC. Financial support from Janssen Research and Development, LLC, for the workshop was in an unrestricted grant to JDRF. JDRF provided participants with transportation, lodging, and meals to attend the workshop. No additional support was provided for the writing of the manuscript. R.D.L. is supported by a grant from the European Union (contract no. QLGi-CT-2002-01886). C.E.-M. is supported by National Institute for Health Research grants R01 DK093954, R21DK11 9800, U01DK127786, R01DK127308, and P30DK 097512; VA Merit Award I01BX001733; JDRF grant 2-SRA-2019-834-S-B; and gifts from the Sigma Beta Sorority, the Ball Brothers Foundation, and the George and Frances Ball Foundation. R.B. is supported in part by the Italian Ministry of University and Research (project code 20175L9H7H). A.G.J. is funded by a National Institute for Health Research (NIHR) Clinician Scientist fellowship (CS-2015-15-018). L.S.P. is supported in part by U.S. Department of Veterans Affairs (VA) awards CSP #2008, I01 CX001899, I01 CX001737, and Health Services Research & Development IIR 07-138; National Institute for Health Research awards R21 DK099716, R18 DK066204, R03 AI133172, R21 AI156161, U01 DK091958, U01 DK098246, and UL1 TR002378; and Cystic Fibrosis Foundation award PHILLI12A0.

R.D.L. received unrestricted educational grants from Novo Nordisk, Sanofi, MSD, and AstraZeneca. C.E.-M. has participated in advisory boards for Dompé Pharmaceuticals, Provention Bio, MaiCell Technologies, and ISLA Technologies. C.E.M. is the recipient of in-kind research support from Nimbus Pharmaceuticals and Bristol Myers Squibb and an investigator-initiated research grant from Eli Lilly and Company. J.F.-B. and J.L.D. were employed by JDRF during the workshop and early stages of writing. J.F.-B. is currently an employee of Provention Bio, and J.L.D. is currently an employee of Janssen Research and Development, LLC. R.B. participated in advisory boards for Sanofi and Eli Lilly and received honoraria for speaker bureaus from Sanofi, Eli Lilly, AstraZeneca, Novo Nordisk, and Abbott. L.S.P. has served on scientific advisory boards for Janssen and has or had research support from Merck, Pfizer, Eli Lilly, Novo Nordisk, Sanofi, PhaseBio, Roche, AbbVie, Vascular Pharmaceuticals, Janssen, GlaxoSmithKline, and the Cystic Fibrosis Foundation. L.S.P. is also a cofounder and officer and board member and stockholder for a company, Diasyst, Inc., that markets software aimed to help improve diabetes management. No other potential conflicts of interest relevant to this article were reported.

The sponsors had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, and preparation, review, or approval of the manuscript. This work is not intended to reflect the official opinion of the VA or the U.S. Government.

Author Contributions. R.D.L., C.E.M., J.F.-B., and J.L.D. conceived of the article and wrote and edited the manuscript. All other authors were involved in the writing and editing of the manuscript. R.D.L. and C.E.-M. are guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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Comment on the role of interferons in the pathology of beta cell destruction in type 1 diabetes. Reply to Lenzen S [letter]

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• Decio L. Eizirik 1 ,
• Priscila L. Zimath   ORCID: orcid.org/0000-0003-2798-7370 1 ,
• Xiaoyan Yi 1 ,
• Arturo Roca Rivada 1 &
• Sarah J. Richardson 2  

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Eizirik, D.L., Zimath, P.L., Yi, X. et al. Comment on the role of interferons in the pathology of beta cell destruction in type 1 diabetes. Reply to Lenzen S [letter]. Diabetologia (2024). https://doi.org/10.1007/s00125-024-06269-3

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Home > HARVEY_MUDD > HMC_STUDENT > HMC_THESES > 231

HMC Senior Theses

Mathematical modeling of type 1 diabetes.

Gianna Wu , Harvey Mudd College/Pomona College Follow

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Lisette de Pillis

Blerta Shtylla

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Type 1 Diabetes (T1D) is an autoimmune disease where the pancreas produces little to no insulin, which is a hormone that regulates blood glucose levels. This happens because the immune system attacks (and kills) the beta cells of the pancreas, which are responsible for insulin production. Higher levels of glucose in the blood could have very negative, long term effects such as organ damage and blindness.

To date, T1D does not have a defined cause nor cure, and research for this disease is slow and difficult due to the invasive nature of T1D experimentation. Mathematical modeling provides an alternative approach for treatment development and can greatly advance T1D research. This thesis describes both a single-compartment and multi-compartment model for Type 1 Diabetes.

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Wu, Gianna, "Mathematical Modeling of Type 1 Diabetes" (2019). HMC Senior Theses . 231. https://scholarship.claremont.edu/hmc_theses/231

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Type 1 Diabetes Research At-a-Glance

The burden of type 1 diabetes remains substantial, and more research is needed to improve the lives of people with type 1 diabetes and to find a cure. To this end, ADA-funded research continues to drive progress by funding research projects topics spanning technology, islet transplantation, immunology, improving transition to self-management and much more. For specific examples of projects currently funded by the ADA, see below.

Martin John Hessner, PhD

Medical College of Wisconsin

Project: Probiotic normalization of innate immunity in type 1 diabetes

"My passion for immunology and type 1 diabetes stems from a personal interest in autoimmunity. This award enables the translation of our rodent studies where we have delayed and prevented type 1 diabetes by modulating the intestinal microbiota, to newly diagnosed patients."

The problem: The human intestinal tract harbors billions of bacteria – collectively termed the microbiome – which representing a significant environmental contact. Evidence suggests that modern lifestyles may promote growth of an altered, sub-optimal intestinal flora. While development of type 1 diabetes (T1D) has a strong genetic basis, this potentially modifiable environmental factor may explain the increase in T1D incidence observed over the past half-century.

The project: This project extends studies of an elevated inflammatory state that is present in T1D patients as well as their healthy family members. In animal models of T1D, dietary or antibiotic protocols that promote growth of anti-inflammatory bacteria delay/prevent development of T1D. Dr. Hessner’s ADA-funded project will test whether probiotic supplementation reduces inflammation and improves insulin secretion in people newly diagnosed with T1D.

The potential outcome: This project has the potential to identify a safe, broadly applicable, environmental modifier that influences T1D progression, which is critical for development of preventive and therapeutic approaches.

Elizabeth D. Cox, MD, PhD

University of Wisconsin-Madison

Project: Identifying actionable self-management barriers for adults with T1D

“I’m a pediatrician by training, and most of my work has focused on improving the way healthcare is delivered to children with diabetes and other chronic diseases. With this grant from the American Diabetes Association, I will be able to build on what I have learned in studying kids and expand that work to improve the healthcare provided to adults with type 1 diabetes.”

The problem: Many adults with type 1 diabetes (T1D) want self-management help. The researchers have previously designed a self-management help questionnaire for youth with T1D that uses child and parent answers to understand the self-management challenges the family faces.  The children and their parents then receive resources designed specifically for their needs. This has been very successful. However, no such program exists for adults with T1D.

The project: Dr. Cox now plans to institute design a self-help management program specifically for adults with T1D. Based on feedback from adults with T1D, she will design a questionnaire by working directly with adults with T1D. Following this, the researchers will then test whether the answers predict important things like how often someone checks their blood sugar, how well their diabetes is controlled, or how well their life is going.  

The potential outcome: At the conclusion of this project, the Dr. Cox and her team expect to have a survey that doctors and nurses can use to recommend self-management help that might best meet the patient's needs.  Being able to recommend specific types of self-management help is expected to improve the lives of adults with T1D.

Karen Cerosaletti, PhD

University of Washington

Project: Single cell RNAseq analysis of islet antigen reactive memory CD4 T cells during T1D progression and therapy

“My interest is understanding how the immune system malfunctions in type 1 diabetes so that we can more accurately predict who will develop type 1 diabetes, when they will develop it, and how we can prevent or block disease onset or progression. This grant will allow us to determine the role of T cells of the immune system in destroying pancreatic islets, resulting in loss of insulin secretion.”

The problem: Type 1 diabetes (T1D) results from immune destruction of insulin-producing islets in the pancreas. The researchers have identified a specific variant of an immune cell, called a T cell, that is found in patients with T1D but not in control people. However, it is unclear to what degree this specific immune cell type is responsible for the initiation and progression of T1D.

The project: The goal of Dr. Cerosaletti and her team is to investigate the role of this unique immune cell in people at high-risk for developing T1D and in patients recently diagnosed with T1D. She will use powerful new technologies to determine how these cells change over the course of T1D progression and assess if these cells play a role in the destruction of insulin-producing islets.

The potential outcome: The significance of this study to T1D is two-fold: the results will advance the understanding of the immune mechanisms underlying the destruction of the insulin producing islets in T1D. Further, these findings will determine the feasibility of using this unique subset of islet T cells as markers to predict or monitor disease onset and progression during T1D.

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Immune Mechanisms and Pathways Targeted in Type 1 Diabetes

Laura m. jacobsen.

1 Department of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA

Brittney N. Newby

2 Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, 1275 Center Drive, Biomedical Sciences Building J-589, Box 100275, Gainesville, FL 32610, USA

Daniel J. Perry

Amanda l. posgai, michael j. haller, todd m. brusko.

Author Contributions LMJ, BNN, DJP, ALP, and MJH wrote the manuscript; TMB conceived of and wrote the manuscript.

Purpose of Review

The immunosuppressive agent cyclosporine was first reported to lower daily insulin dose and improve glycemic control in patients with new-onset type 1 diabetes (T1D) in 1984. While renal toxicity limited cyclosporine’s extended use, this observation ignited collaborative efforts to identify immunotherapeutic agents capable of safely preserving β cells in patients with or at risk for T1D.

Recent Findings

Advances in T1D prediction and early diagnosis, together with expanded knowledge of the disease mechanisms, have facilitated trials targeting specific immune cell subsets, autoantigens, and pathways. In addition, clinical responder and non-responder subsets have been defined through the use of metabolic and immunological readouts.

Herein, we review emerging T1D biomarkers within the context of recent and ongoing T1D immunotherapy trials. We also discuss responder/non-responder analyses in an effort to identify therapeutic mechanisms, define actionable pathways, and guide subject selection, drug dosing, and tailored combination drug therapy for future T1D trials.

Introduction

Immunotherapy for type 1 diabetes (T1D) has been the subject of more than three decades of investigation directed toward the prevention and treatment of this disease. While it is not yet feasible to persistently halt autoimmunity, considerable progress has been made in our ability to predict T1D, understand underlying immune mechanisms, and slow the rate of β cell decline. Longitudinal clinical trials [ 1 – 8 ] ( Table 1 ) together with cross-sectional investigation of human pancreas (e.g., the Network for Pancreatic Organ donors with Diabetes (nPOD) [ 22 , 23 ]) are reshaping our understanding of the natural history of T1D and human pancreas pathology. Indeed, these studies have laid the groundwork for a new phase of clinical trials that will, for the first time, enable a previously unprecedented capacity for precision medicine harnessing genetic and peripheral biomarkers to target patient-specific immune and β cell pathways. Herein, we review known and emerging biomarkers of T1D pathogenesis, their role in guiding immunotherapy, lessons garnered regarding the functional mechanisms of past and current intervention trials, and ways to focus and optimize future cutting-edge treatment modalities.

Longitudinal follow-up in infants and early prevention trials in individuals with high genetic risk for T1D

AAb , autoantibodies, FDR , first-degree relative, ECL , electorchemiluminescenc

Autoimmune Pathogenesis of T1D

There is a general consensus that T1D is a complex and heterogeneous disease, yet debate remains as to the driving etiology behind disease progression. That said, the presence of insulin- and GAD-specific autoreactive lymphocytes in peripheral blood and the confirmation of T and B cell infiltrates in human islets before and persisting after disease onset clearly support an autoimmune pathogenesis [ 24 – 26 , 27•• , 28 , 29 ]. Diabetogenic lymphocytes are thought to initiate a cascade that bypasses normal regulatory checkpoints resulting in epitope and antigenic spreading, the emergence of multiple autoantibodies (AAb), and progressive β cell destruction ( Fig. 1 ), with various steps throughout this process likely permitted or aggravated by T1D risk loci.

Pathways involved in T1D pathogenesis and targeted therapies tested to date. Although prior trials with immunotherapies have not resulted in remission of T1D, numerous trials have been successful in transiently altering the landscape of islet autoimmunity yielding valuable mechanistic lessons that can help guide future therapeutic intervention. Harnessing the tolerogenic power of the gut or formulations for peripheral immunization provides the basis for antigen-specific therapies, such as oral insulin and GAD-alum, which aim to induce antigen-specific CD4 + CD25 high FOXP3 + Tregs and, secretion of tolerogenic cytokines resulting in reduced islet-specific T eff and CTL responses (1). Several immunotherapeutics are directed at depleting autoreactive T cells and/or thwarting their activation, which may lead to a chronic exhaustion of T effs and induction of tolerance. These include methyldopa (2), teplizumab (anti-CD3) (3), ATG (4), Alefacept (LFA-3/Fc fusion protein targeting CD2) (5), and Abatacept (CTLA-4/Fc fusion protein blocking CD80/86–CD28 interaction) (6). Stimulation of T regs has been the subject of several trials. Low-dose IL-2 preferentially sustains T reg expansion (7), while replenishing the T reg compartment through adoptive cell transfer may serve to restore immunoregulation in T1D (8). B cell depletion via rituximab (anti-CD20) seeks to impede B cell-mediated antigen presentation and activation of diabetogenic T cells (9). Proinflammatory cytokine blockade may act to prevent deleterious effects on β cell survival and function in the islet microenvironment (10), while β cell-specific therapies that promote survival may avert loss of β cell mass and function (11). (This figure is original but was created, in part, with adapted art images from Servier Medical Art, from Creative Commons user license Attribution 3.0 Unported [CC BY 3.0]; https://creativecommons.org/licenses/by/3.0/ )

Genetics as the Basis for Understanding Disease Mechanisms

T1D incidence is 15–20 times higher in first-degree relatives (FDR) of people with T1D compared with unaffected relatives [ 30 , 31 ]. Additionally, T1D concordance in monozygotic twins is about 65% by age 60 versus ~ 6–7% between dizygotic twins and non-twin siblings [ 31 – 33 ]. Of the 57 currently identified loci associated with T1D risk (curated at http://www.immunobase.org ) [ 34 , 35 ], the human leukocyte antigen (HLA) locus predominates risk (~ 50%) with all other loci contributing minimally (odds ratios (OR), ~ 1.10–2.38) [ 36 – 38 ]. The HLA is the most polymorphic locus in the human genome, and haplotypes ranging from highly susceptible to highly protective have been reviewed extensively [ 30 , 39 ]. Notably, the HLA DR4-DQ8 and DR3-DQ2 allele types confer the most risk with OR of 11 and 3.6, respectively, and an additive OR of ~ 40 in individuals carrying both haplotypes [ 31 , 39 ]. Generally, the pathogenic mechanisms by which HLA mediates T1D are thought to revolve around antigen presentation, but whether it is due to central tolerance/thymic selection or T cell activation in the periphery is not fully elucidated. Although intensely studied, the pathogenic mechanisms by which HLA and non-HLA genes collectively mediate T1D are not conclusively known. Several individual loci do have proposed mechanisms (reviewed in [ 30 , 31 ]) with the preponderance of candidate genes having functional immunological roles, including genotype:phenotype associations impacting T cell receptor (TCR), co-receptor, and cytokine signaling pathways. Hence, genetics not only influence who will develop T1D but also which etiologies or T1D “endotypes” (i.e., subtypes of the condition defined by distinct pathophysiological mechanisms encompassing a person’s clinical features and response to treatment [ 27•• , 40 ]) are manifested by individuals following development of islet autoimmunity, altogether resulting in disease heterogeneity.

Initiation of islet autoimmunity is associated with HLA, where children up to 6 years of age carrying DR4-DQ8 tend to seroconvert to insulin AAb (IAA), while those carrying DR3-DQ2 tend to seroconvert to GAD65 AAb (GADA) [ 41 ]. Therefore, interventions designed to prevent islet autoimmunity rely heavily on genetic risk for cohort stratification. To date, this has largely been done by identifying individuals with FDRs affected by T1D who also carry one or more of the high-risk HLA loci [ 42 ]. However, since the vast majority of individuals with T1D have no family history of T1D, such strategies miss large swaths of subjects that eventually progress to T1D. Conversely, relying on HLA alone to stratify the general population (as opposed to family-based) cohorts identifies too many false positives having insufficient specificity for T1D progressors [ 30 , 43 , 44 ]. Influences for non-HLA genes on progression from multiple AAb to clinical disease have also been shown [ 45 – 47 ]. Additionally, a recent genome wide association study (GWAS) described novel associations between age at T1D diagnosis and the 6q22.33 chromosomal region encoding protein tyrosine phosphatase receptor kappa ( PTPRK ) and thymocyte-expressed molecule involved in selection ( THEMIS ), both of which serve critical roles in thymic T cell development [ 48 ]. To assist in cohort stratification, cumulative genetic risk score (GRS) models measuring both HLA and non-HLA risk have been constructed via logistic regression algorithms. These GRS models demonstrate that the inclusion of non-HLA loci into a cumulative risk score increase model accuracy for classification of subjects as patients or controls [ 31 , 49 ]. Further, they are able to predict progressors [ 49 , 50 ], discern T1D from other forms of diabetes [ 51 , 52 ], and describe the prevalence of T1D onset in individuals over 30 years of age [ 53 ]. Recently, it was shown in the prospective TEDDY study cohort that a GRS model can improve the identification of infants with >10% risk of developing multiple AAb by age 6 years versus the population risk of 0.4% [ 4 ]. With further refinement, validation, and longer follow-up, GRS models have the potential to serve as standard clinical tools to greatly enhance the feasibility of primary prevention trials, particularly as genotyping costs continue to decline making population-based screenings feasible. Importantly, widespread adoption will depend on the discovery of interventions specifically targeting disease-associated pathways.

Serological and Cellular Biomarkers Support an Autoimmune Pathogenesis

Serological biomarkers.

Seroconversion to multiple AAb positivity is currently the most reliable predictor of T1D progression [ 54 ] ( Table 2 ). Radioimmunoassays (RIA) are the gold standard for detection of the major autoantibodies: IAA, GADA, insulinoma-associated protein 2 AAb (IA-2A), and zinc transporter 8 AAb (ZnT8A) [ 55 – 58 ], and augmentation of current T1D GRS models with RIA-based AAb assays or multiplexed electrochemiluminescence (ECL) assays shows promise and should prove useful in identifying subjects who are most likely to benefit from early immunotherapeutic intervention [ 50 , 89 ].

Biomarkers of type 1 diabetes risk and progression

AAb , autoantibodies; RIA , radioimmunoassay; ELISA , enzyme-linked immunosorbant assay; ECL , electrochemiluminescence; IAA , insulin autoantibody; GADA , GAD65 autoantibody; IA-2A , insulinoma-associated protein-2 autoantibody; ZnT8A , zinc transporter 8 autoantibody; IASP , Islet Autoantibody Standardization Program; PI:C ratio , serum proinsulin to C-peptide ratio; cfDNA , cell-free DNA; NGS , next-generation sequencing; miRNA , microRNA; TCR , T cell receptor; AIRR , adaptive immune receptor repertoire; CDR3 , complementarity determining region 3

Given the mounting evidence for intrinsic β cell stress in T1D pathophysiology [ 90 ], biomarkers measuring β cell dysfunction or death, such as elevated fasting serum proinsulin: C-peptide ratio [ 67 – 74 ] and elevated unmethylated INS cell-free DNA (cfDNA) [ 75 – 80 ] ( Table 2 ), may also improve screening and inform therapeutic interventions aimed at interdicting autoimmunity (reviewed below), promoting β cell survival/expansion/replacement (e.g., verapamil [ 91 ], stem cell therapy [ 92 ]), reducing β cell stress (e.g., etanercept [ 93 ]), and restoring β cell function (e.g., Gleevec [ 94 ], sitagliptin [ 95 ]). The short half-life of cfDNA in circulation is a notable limitation, but studies investigating methylation patterns in other β cell genes are underway, offering the potential to combine these assays into methylation signature panels [ 96 , 97 ]. Furthermore, circulating unmethylated INS cfDNA and/or PI:C ratio could serve as outcome measures. As but one example from a clinical trial, a significant decline in unmethylated INS cfDNA was observed at 1 year in teplizumab-treated subjects versus placebo [ 76 ]. Given that unmethylated INS cfDNA and/or PI:C ratio biomarkers assess β cell death and dysfunction in real-time, combining these assays with immunophenotyping and large-scale-omics may guide future therapeutic selections.

Detection of β cell stress in those with high GRS and/or AAb positivity may facilitate identification of subjects who will likely benefit from drugs aimed at preventing effector T cell (T eff ) activation or co-stimulation [ 98• , 99 – 101 , 102• , 103 – 105 , 106• , 107 ] alone or in conjunction with therapies promoting β cell survival/replication (e.g., small molecules studied in vitro and in vivo [ 108 ]). Finally, various microRNAs (miRNAs) are dysregulated in the circulation of T1D patients [ 86 , 109 ] and have been shown to regulate β cell function (reviewed in [ 110 ]; Table 2 ). While still developmental in terms of application to clinical trials, exosomes as well as free miRNAs represent a new class of biomarkers to potentially improve the prediction of T1D and differentiate disease etiologies.

Cellular Biomarkers

Novel methods for isolation, expansion and characterization of islet-infiltrating lymphocytes are offering new insights on T cell clones with specificity for neoepitopes, such as defective ribosomal products (DRiPs) and hybrid insulin peptides (HIPs) as well as those previously identified [ 111 – 117 ]. A multitude of data on the diversity of the T cell repertoire as well as information regarding evolution of clonal expansion within the islet microenvironment [ 24 , 63 , 117 ] provide promising avenues for epitope discovery, functional analysis of islet-reactive T cells, and development of novel biomarkers of autoreactivity ( Table 2 ). As next-generation sequencing (NGS) continues to become more affordable, adaptive immune receptor repertoire (AIRR) analyses may emerge as a robust tool to identify predictive biomarkers of T1D; moreover, AIRR may guide development of tailored antigen-specific therapies, TCR-redirected regulatory T cells (T reg ) [ 118 , 119 ], and drugs targeting T cell autoreactivity.

Clinical Interventions and Mechanisms of Action

Prevention of T1D is predicated on early identification of high-risk subjects and application of an effective intervention prior to disease progression. In support of this notion, T1D was recently reclassified, with consensus support from the JDRF, Endocrine Society and American Diabetes Association, to include three distinct stages, two of which are “preclinical.” Specifically, stage 1 T1D is defined by the presence of two or more AAb and normal glucose metabolism, while stage 2 is defined by AAb positivity with impaired glucose metabolism. Stage 3 T1D is defined by the onset of clinical or symptomatic disease ( Table 3 ) [ 120 ]. Over the past 25 years, most prevention efforts have targeted antigen-based or generally regarded as safe (GRAS) interventions. However, given the lack of efficacy noted with these approaches, more potent immune-altering agents are now being considered.

Classification of type 1 diabetes stages related to autoimmunity and dysglycemia [ 120 ]

Primary Prevention

GRAS supplements administered to genetically at-risk infants prior to the development of autoimmunity were the targets of the T1D TrialNet NIP and ENDIT trials [ 12 , 14 ] while TRIGR, BABYDIET, and FINDIA attempted to modulate or eliminate early exposures to potentially antigenic components (i.e., gluten or bovine insulin in infant formula [ 8 , 121 , 122 ]) ( Table 1 ). Sadly, none of these trials showed efficacy in delaying or preventing T1D [ 8 ]. That said, neither antigen-specific nor immunomodulatory therapies have been tested in the primary prevention setting (before the presence of islet AAb). Notably, the Pre-POINT study, which only had a mechanistic outcome [ 13 ], recently utilized high-dose oral insulin therapy in high-risk HLA, AAb-negative FDRs and elicited a mucosal anti-insulin IgA response, an IgG response, and promoted CD4 + FOXP3 + CD127 − T reg cells. These data support the notion that exposure of the intestinal epithelium to T1D-specific autoantigens prior to seroconversion and initiation of islet autoimmunity may induce tolerance via TGF-β and IL-10 producing DCs that subsequently drive T reg responses to the same tolerized antigens [ 13 , 123 ]. Recent and ongoing analyses of longitudinal data generated from the DIPP and TEDDY cohorts are unveiling pre-seroconversion biomarkers (e.g., gut microbiome, metabolomics, lipidomic, and proteomic profiles) of eventual T1D progression that might improve our ability to determine candidates for early antigen specific or immunomodulatory therapy [ 124 – 128 ].

Secondary Prevention

Formulations of islet autoantigens have been tested as “vaccinations” in secondary prevention in an effort to induce tolerance through promotion of T reg and downregulation of autoreactive T eff . Specifically, oral or intranasal preparations of insulin and insulin peptides are thought to encounter the gut-associated lymphoid tissue (GALT) or mucosa and with repeated exposure, induce insulin-specific T reg [ 17 ] capable of suppressing insulin-reactive T eff and CTLs via secretion of regulatory and anti-inflammatory cytokines (e.g., IL-10, IL-4, TGF-β, etc.), IL-2 competition, and through cell contact-dependent mechanisms ( Fig. 1 ) [ 118 , 129 – 133 ].

The prevention of spontaneous and adoptive cell transfer of autoimmune diabetes in rodent models [ 123 , 134 – 137 ] through exposure to oral insulin led the way for several large clinical trials using insulin as a primary target-antigen through oral and intranasal routes of administration (efficacy and mechanistic outcomes are detailed in Table 1 and reviewed in [ 138 ]). Unfortunately, no individual study has been able to meet primary endpoints to delay or prevent T1D in those at risk [ 13 , 17 , 18 , 20 , 139 ]. That said, these trials do suggest that oral or intranasal insulin may elicit tolerogenic immune responses capable of delaying T1D in specific subsets of individuals [ 17 , 18 , 20 ]. Specifically, of subjects with high IAA titers, those with loss of first phase insulin response demonstrated delayed progression and potential benefit from oral insulin [ 20 ]. In DIPP, those likely to progress had higher levels of insulin-specific IgG1 and IgG3 [ 15 ], potentially necessitating addition of a synergistic therapy when that mechanistic outcome is seen. As such, the ongoing development of biomarkers that prospectively identify these subgroups may allow primary prevention trials of oral insulin alone or in conjunction with pre-conditioning agents (e.g., anti-thymocyte globulin (ATG), anti-CD3, or Abatacept).

Similar to insulin, the use of GAD bound to an aluminum hydroxide adjuvant (GAD-alum) has been unsuccessful in preventing progression to T1D in at-risk children with multiple AAb [ 21 ]. New-onset intervention trials, which also failed to preserve β cell function, demonstrated GAD-specific immune responses including elevated GAD AAb levels [ 140 , 141 ], increased GAD-induced secretion of IL-5, IL-10, IL-13, IL-17, IFNγ, and TNF-α, but not IL-6 or IL-12 by PBMC [ 140 , 142 ], increased CD4 + CD25 high FOXP3 + T reg cell frequency [ 143 ], reduced CD4 + CD25 + T cell frequency [ 143 ], as well as increased FOXP3 and TGF-β mRNA expression in whole PBMC [ 140 , 142 ] ( Table 1 ). Interestingly, in the new-onset trial, a subgroup of clinical responders (defined as < 10% loss of AUC C-peptide from baseline) exhibited higher GAD-induced secretion of Th2-associated cytokines (IL-5 and IL-13) 1 month after treatment ( Fig. 1 ) [ 142 , 143 ]. These findings again suggest that biomarkers capable of selecting responders could support future GAD-directed trials especially when considered in combination with agents having complementary/synergistic mechanisms of action (MOA).

T Cell-Directed Therapy

Tcells play an essential role in disease progression in the NOD mouse and agents have been used to both target T eff populations and prevent the acquisition of autoreactive memory T cells in human T1D. TrialNet’s teplizumab (anti-CD3) ( NCT01030861 ) and abatacept (CTLA-4 Ig) ( NCT01773707 ) secondary prevention trials in at-risk relatives (stage 1 or 2 T1D) are currently ongoing. The mechanisms at play in each of these trials are detailed further below and in Fig. 1 , but T cell-directed therapies are a logical step in the prevention arena to interdict before critical loss of β cells.

Intervention in New-Onset or Established T1D with Evidence Toward Efficacy

Because functional β cell mass declines precipitously during the first year or longer following T1D onset (stage 3) [ 144 , 145 ], numerous therapeutics have been trialed in recently diagnosed patients [ 98• , 99 – 101 , 102• , 103 , 105 , 106• , 107 , 146 – 162 ]. While an in-depth discussion of each interventional approach is beyond the scope of this review, we have summarized recent interventional approaches in Table 4 and their proposed mechanisms in Fig. 1 ). Below, we review selected interventional approaches that are most likely to be guided by the application of novel biomarkers.

Mechanisms targeted and clinical response in trials for treatment of stage 3 (new onset) T1D

ATG , anti-thymocyte globulin; G-CSF , granulocyte-colony stimulating factor; TN , Type 1 Diabetes TrialNet; T reg , regulatory T cell; T n , naïve T cell; T cm , central memory T cell; T em , effector memory T cell; CRP , C-reactive protein; SAA , serum amyloid A

Mechanisms of Action of T Cell Targeting Agents

Emerging data suggest that immunotherapeutics aimed at depleting T eff and CTL (i.e., anti-CD3, ATG, Alefacept) harbor more intricate immunomodulatory mechanisms than originally postulated. For instance, responders to Teplizumab (anti-CD3) exhibited a partial T cell exhaustion phenotype that was not terminally differentiated but characterized by expression of KLRG1, TIGIT, and EOMES [ 166•• ]. Following Alefacept treatment, Rigby et al. observed a temporary downregulation of T em with their recovery after 24 months corresponding to a decline in C-peptide parallel to that of placebo [ 167• ]. Similarly, mechanistic data derived from the ATG/G-CSF pilot trial revealed increased frequencies of FOXP3 + Helios + T regs with concomitant augmentation of PD-1 expression for up to 18 months following treatment, which correlated with C-peptide AUC in responders [ 98• ]. Further, this combinatorial therapy increased CD16 + CD56 high NK cells, a phenotype associated with immunoregulatory and tolerogenic properties [ 98• , 168 , 169 ].

T reg Promoting Agents and Cellular Therapies

IL-2 signaling plays a non-redundant role in T reg development, serving as a prime therapeutic target to augment T reg responses in T1D ( Fig. 1 ). While initial attempts at targeting this pathway using high dose IL-2 and rapamycin transiently induced T reg expansion, this trial was plagued by deleterious effects on β cell function as a result of toxicity induced by rapamycin and concomitant expansion of NK cells and eosinophils [ 170 ]. More recently, safety and dose-finding investigations of low-dose IL-2 regimens have shown more promise, preferentially sustaining T regs with no detrimental effects on glucose metabolism [ 153 , 154 ].

As a logical progression in the immunotherapy space, adoptive cell therapies (ACT), such as those used in settings of graft-versus-host disease (GVHD) [ 171 – 175 ], are under study in T1D [ 118 , 163 , 176 ] ( Fig. 1 ). Approaches under preclinical or clinical investigation include mesenchymal stem cells (MSC) [ 177 – 180 ], embryonic stem cells (ESCs) [ 181 ], induced pluripotent stem cells (iPSCs) [ 182 , 183 ], and T reg cellular therapy (reviewed in [ 118 ]) derived from peripheral blood, bone marrow, adipose tissue, or umbilical cord blood (UCB) [ 176 ]. An ongoing multicenter trial using autologous peripheral blood-derived T regs ( NCT02691247 ) has completed enrollment, and trials of ex vivo expanded autologous UCB-derived T regs in new-onset T1D patients are being planned [ 176 ]. From these trials, we may be able to derive responders and non-responders to autologous T reg ACT, whether due to low numbers or poor function of T regs Mechanistic studies and biomarker discovery efforts may drive our ability to generate tailored T reg therapies. For example, in individuals with low T reg numbers, ex vivo T reg expansion and reinfusion may be sufficient, whereas for those with impaired T reg function, lentiviral transfection or CRISPR-Cas9 gene editing may be used to restore T reg signaling or stabilize FOXP3 expression and suppressive capacity. Moreover, suppression via chimeric antigen receptor (CAR) technology or TCR redirected T reg “avatars” [ 119 ] may be most effective at targeting T regs to the organ of interest and avoiding off-target immune suppression.

Trimolecular Targets

The presentation of islet auto-antigenic peptides via MHC molecules to the TCR is an essential driver for activation of autoreactive T cells [ 117 ]. Thus, blockade of this interaction has been the subject of intense investigation. Use of molecular docking screens led to the identification of methyldopa as a therapeutic for preventing recognition of proinsulin peptides presented in the context of the high-risk HLA-DQ8 molecule [ 184 ]. Methyldopa is now under investigation in at-risk individuals ( NCT03396484 ), and studies are ongoing to identify blocking agents for other autoantigen-HLA combinations.

Advancing β Cell Survival and Function

Protecting β cells from inflammatory onslaught represents an additional avenue for T1D intervention. The cytokine milieu within the local islet microenvironment not only serves to direct the crosstalk between innate and adaptive cells, but these soluble factors may have direct deleterious effects on pancreatic β cell function and survival. As such, functional blockade of pro-inflammatory cytokines, such as IL-6, IL-1β, IL-12, and TNF-α, may serve to curtail local islet inflammation and cytokine-induced β cell death [ 93 , 155 – 157 , 185 ]. Additionally, a trial has been initiated testing the utility of Gleevec (imatinib mesylate), which is well known for the treatment of chronic myelogenous leukemia (CML), as a β cell restorative therapy ( NCT01781975 ). This tyrosine kinase inhibitor has been shown to improve β cell function in NOD β cells (by inhibiting negative regulation of insulin secretion) and insulin sensitivity in CML patients with type 2 diabetes [ 94 , 186 ]. Moreover, through inhibition of Bcr-Abl, Gleevec is able to mitigate downstream activation of phosphatidylinositol 3-kinase signaling, endoplasmic reticulum stress, and cytokine-induced β cell death [ 187 , 188 ]. Finally, stem cell differentiation into insulin-producing cells might eventually facilitate β cell replacement without the need for islet isolation from HLA compatible organ donors [ 92 , 189 , 190 ]. Best implemented in combination with immunomodulatory treatment, stem cell derived β-like cells would be particularly useful in patients with low stimulated C-peptide production (indicative of functional β cell mass), but challenges associated with terminal differentiation, phenotyping and function have slowed progress toward their clinical application (recently reviewed [ 191 ]).

Drug Response and Responder/Non-responder Analyses

Subgroups of T1D patients identified based on lymphocytic profiles in peripheral blood and within the pancreatic islets suggested that B cell or Tcell-targeting immunotherapies may have utility in specific cohorts [ 27•• ]. Indeed, in the past decade, clinical trials have demonstrated transient efficacy in new-onset T1D and enabled the designation of subjects as clinical (i.e., preservation of baseline AUC C-peptide) or immunological responders (i.e., alteration of the immunophenotype) and non-responders. The rituximab (anti-CD20) and teplizumab/otelixizumab (anti-CD3) trials tended toward greater clinical response in younger subjects [ 102• , 103 , 147 – 149 ]; whereas, the opposite was seen for the ATG (responders were 22–35 years old) and pilot ATG/G-CSF studies (responders’ mean age was 27.5 years) ( Table 4 ) [ 98• , 99 – 101 ]. In the Abatacept trial, there was a lack of effect seen in HLA-DR3-negative subjects (unrelated to age) [ 104 ], supporting the use of genetic determinants to guide treatment selection. Outliers also provide unique opportunities to understand mechanism. For example, from the ATG/GCSF study in established T1D, four subjects maintained C-peptide above baseline beyond 24 months and mechanistically, were found to have a transient increase in FOXP3 + Helios + T reg at 6 months [ 98• ]. Conversely, rare placebo-treated subjects demonstrate higher C-peptide at study endpoint than baseline [ 99 , 100 ] suggesting a need for sufficiently powered studies to determine the clinical, genetic, and immunologic features of these cases. Because definitions of “responder” and the type/complexity of data collected vary between trials, data can rarely be compared across studies. Moreover, not all individuals will respond to the same MOA suggesting that varied disease endotypes may be at play. Interestingly, some consistencies were found across trials: (1) subjects with higher baseline C-peptide perform better and (2) the eventual outcome after cessation of therapy (i.e., gradual decline in β cell function parallel to the placebo group) seems inevitable with current modalities. Altogether, these findings support initiatives to intervene early in the disease, to re-treat at intervals based on immune and metabolic biomarkers, and to implement combination therapies targeting multiple MOA in T1D. On the other hand, the observation that many patients maintain detectable levels of stimulated C-peptide for at least a few years after diagnosis suggests that meaningful benefit may also be possible even at later time points [ 144 ], and indeed, a recent study enrolled patients who had disease for up to 2 years [ 98• , 99 ].

To better understand how to provide personalized and efficacious care, the field may benefit from further defining distinct endotypes to predict how subjects with T1D will respond to different immune therapies. The currently available mechanistic data can be used to pick the next therapeutic agent(s), beginning with small trials and mechanistic endpoints. The establishment of a database and structured analytical pipeline for higher order analyses and future cross-trial comparisons is needed. With this, there is a need to standardize a “minimum mechanistic analysis” (e.g., human immunophenotyping by flow cytometry, GRS, functional and epigenetic analyses of polyclonal and antigen-specific T and B cells) in addition to assays for β cell function/survival to facilitate post hoc analyses, potential discovery of unexpected MOAs, and to eventually enable individualized biomarker-informed treatment decisions.

Optimizing Timing and Combinatorial Therapies

Lessons from immunotherapy/combo therapy in other autoimmune diseases and cancer.

To move immune therapy for T1D forward, there is a need to adopt lessons learned from the treatment of other autoimmune diseases. For example, inflammatory bowel disease (IBD), juvenile idiopathic arthritis (JIA), and rheumatoid arthritis (RA) not only have standardized clinical and mechanistic outcomes, but also apply combination therapies, re-treatment for continued immune modulation, as well as therapies based on endotype determination (e.g., poly-articular JIA versus mono-articular JIA responds to different therapeutic interventions) [ 192 – 194 ]. Cancer immunotherapy protocols similarly employ a mechanism-driven approach based on genetic and other biomarkers (e.g., Gleevec and its specific targeting of the Bcr-Abl chimeric oncogene named the Philadelphia chromosome responsible for over 90% of CML cases) [ 195 ]. Finally, transplant medicine, where immunosuppressive therapies were first developed, now involves immunomodulatory ACT to prevent and treat GVHD [ 196 ] supporting current trials of T reg ACT in T1D. Low-dose IL-2 has also been used in GVHD with promising results [ 197 , 198 ].

Through advances in composite genetic risk models that incorporate HLA and minor risk alleles [ 51 , 52 , 199 ], it may be possible to not only predict T1D but also identify specific pathway targets with sufficient confidence to enact more precise and personalized interventions prior to the development of autoimmunity or β cell dysfunction/destruction. For example, given the known associations between specific HLA haplotypes and first AAb reactivities [ 200 , 201 ], we may be able to select autoantigen-specific or epitope-blocking therapies (e.g., methyldopa) to prevent initial seroconversion or T cell activation in certain high-risk individuals. We expect that a pre-treatment risk assessment with genetic and circulating biomarkers will estimate a person’s likelihood of response with a given therapeutic MOA (e.g., T eff , T reg , B cell, β cell, antigen-specific, or combinations thereof) to inform tailored T1D therapies in the future.

Treatment Timing and Potential Need to Re-dose

Optimizing the timing of intervention(s) may be just as important as drug selection for optimizing clinical efficacy. For example, while rituximab demonstrated only transient benefit in a subset of new-onset T1D patients [ 148 ], early B cell depletion in pre-stage 1 disease may effectively prevent B cell-T cell interactions required for autoimmune activation and islet infiltration [ 202 – 204 ]. Along those lines, antigen-specific therapies, such as insulin and GAD, provided in pre-stage 1 to those with high-risk DR4 and DR3 haplotypes, respectively, may prevent the expansion/activation of early autoreactive clonotypes. Moving forward, we must identify the best biomarkers to direct therapies for each stage of disease (pre-stage 1 T1D though established T1D).

While no permanent alteration to the immune environment has occurred following immunotherapy in T1D, a delay in C-peptide loss by 9 or more months has been reported with B cell and T cell targeting agents [ 98• , 99 , 105 , 106• , 107 , 147 , 148 ]. We expect durable preservation of C-peptide may require retreatments with immunomodulatory agents, potentially with additional agents aimed at inducing durable tolerance to autoantigens. Hence, the safety and efficacy of repeated dosing and/or sequential agent administration needs to be determined, and immune and metabolic biomarkers are needed to establish the appropriate timing for re-treatment, though waning of the tolerogenic T cell profile may serve as a starting point.

Conclusions

Although the prior trials have not resulted in complete remission of T1D, numerous trials have been successful in transiently altering the landscape of islet autoimmunity yielding valuable mechanistic lessons that can help guide future therapeutic intervention. The amalgamation of genetic predisposition and environmental factors alter the equilibrium between immunogenic and tolerogenic responses. Whether T1D occurs through defects in central tolerance, a breakdown in peripheral tolerance, viral infection, altered microbiome, dietary exposures, molecular mimicry, or the combination [ 205 , 206 ] remains to be determined, but there is a clear autoimmune signature marked by autoreactive T and B cell clones that precedes the decline in β cell mass resulting in hyperglycemia [ 24 , 27•• , 207 ]. Ultimately, the common goal for each immunotherapy is to restore adaptive immune balance by promoting not only T reg but potentially driving T cell exhaustion and reducing autoreactive T eff and T em activities. We expect that this will be best achieved by early detection and intervention, along with the use of combination or sequential treatment with antigen-specific, β cell-directed, immunomodulatory, and/or cellular therapies. There is no one size fits all in the treatment of autoimmune disease, and that is especially true of T1D. New and emerging biomarkers will allow for targeted approaches in those with T1D who share common pathogenic mechanisms.

Acknowledgments

The authors would like to thank Dr. Mark A. Atkinson for his comments and critical review of the manuscript.

This effort was supported by grants from the NIH (P01 AI42288 and R01 DK106191 to TMB; F30 DK105788 to BNN), the JDRF (post-doctoral fellowships to LMJ (3-PDF-2018–579-A-N) and DJP (2-PDF-2016–207-A-N)), the Leona M. and Harry B. Helmsley Charitable Trust, and the McJunkin Family Charitable Foundation.

Abbreviations

Conflict of Interest Laura M. Jacobsen, Brittney N. Newby, Daniel J. Perry, Amanda L. Posgai, Michael J. Haller, and Todd M. Brusko declare that they have no conflict of interest.

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Eli Lilly's weekly insulin is running into the same issue Novo Nordisk's did

The pharma giant presented results from the late-stage clinical trials of its once-weekly insulin at an annual diabetes meeting in europe.

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Eli Lilly ( LLY ) said on Tuesday that its experimental weekly insulin, efsitora, worked just as well as daily doses for patients with type 1 diabetes in a late-stage clinical trial. However, the study also found that the drug carried a higher risk of severe low blood sugar — a similar outcome of Novo Nordisk’s ( NVO ) rival drug.

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The pharma giant presented detailed results from the phase 3 clinical trial, QWINT-5, of its weekly insulin efsitora at the European Association for the Study of Diabetes (EASD) annual meeting in Madrid today.

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The QWINT-5 trial involved 692 participants from around the globe with type 1 diabetes that were being treated with daily basal insulin and multiple daily mealtime insulin injections.

Over the course of a year, some of the participants were given weekly doses of efsitora while others were given daily doses of traditional insulin.

After 26 weeks, participants taking the weekly efsitora saw their A1C levels drop an average of 0.53%. For comparison, patients in the trial taking daily insulin saw their A1C levels fall 0.59%. A1C tests measure a patient’s blood sugar levels over a three-month period .

“These results underscore the potential of efsitora to help some people living with type 1 diabetes lower their A1C with only one basal insulin injection per week, while also highlighting the complexity of treating this chronic disease,” said Jeff Emmick, senior vice president of product development at Eli Lilly, in a statement.

However, Eli Lilly noted that serious adverse events were higher among the efsitora group when compared with the daily insulin group. This was driven by severe hypoglycemic — low blood sugar — events.

The estimated rate of severe hypoglycemic events per patient over one year was 0.14 for efsitora and 0.04 for daily insulin.

Eli Lilly-rival Novo Nordisk had the same issue with its experimental, once-weekly insulin Awiqli. This low blood sugar risk for people with type 1 diabetes was one of the reasons Awiqli was rejected by the U.S. Food and Drug Administration earlier this summer.

“Now, we acknowledge that the higher rates of severe hypoglycemia in QWINT-5 are a potential concern,” Paul Owens, an Eli Lilly vice president of global brand development, told Quartz. “Patients should always talk to their healthcare provider and work together with and determine the best treatment option that is best for them.”

He added that the company is continuing to asses the data to determine how to best mitigate this risk.

Eli Lilly also presented today at EASD detailed results of its QWINT-2 trial. This trial found that efsitora also worked just as well as daily insulin on patients with type 2 diabetes. The presentations come less than a week after the pharma company announced top-line results of its other efsitora trials, QWINT-1 and QWINT-3 .

“The results we’ve shared from the collective QWINT program add to the growing body of evidence [on] the potential for once-weekly to be a transformative treatment option for people living with type 2 and some people with type 1 diabetes,” Owens said.

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New hydrogel system could extend semaglutide dosing to once a month for type 2 diabetes patients

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French researchers have developed a new drug delivery system that could cut the dosing schedule for the type 2 diabetes and weight control drug semaglutide to just once a month, according to new research to be presented at this year's annual meeting of The European Association for the Study of Diabetes (EASD), Madrid (9-13 Sept).

"Glucagon-like peptide-1 agonist (GLP-1) drugs have transformed type 2 diabetes care, but weekly injections can be burdensome for patients. A single shot a month could make it much easier for people living with diabetes or obesity to stick to their drug regimens, improving quality of life and reducing side effects and diabetes complications," said lead author Dr. Claire Mégret from ADOCIA, Lyon, France, the biotechnology company who developed the hydrogel.

Semaglutide works by mimicking the hormone glucagon-like peptide 1 (GLP-1), and is currently authorized for the treatment of type 2 diabetes patients with insufficient glycaemic control and long-term weight management.

Clinical studies suggest that adherence to injected semaglutide is 39-67% for type 2 diabetes patients at one year, and 40% for patients who take the drug for weight loss. Similarly, adherence to daily oral pill formulations is around 40% at one year.

Long-acting delivery formulations could increase drug efficacy and safety by maintaining steady drug levels in the body at optimal concentrations.

The new hydrogel delivery platform uses two innovative degradable polymers that are chemically bound to one another to form a gel, but allow slow, sustained release of soluble peptides over 1 to 3 months.

A small dollop of gel, known as a 'depot,' of the semaglutide-laden hydrogel is injected under the skin. The challenge is to formulate the hydrogel to entrap the peptides to limit initial burst (early release) of semaglutide molecules and, at the same time, to allow smooth release and controlled dissolving of the gel over one month, without generating toxic molecules." Dr. Claire Mégret from ADOCIA, Lyon, France

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Several formulations of the hydrogel were tested in vitro to investigate the drug release rate, duration of action, and semaglutide load to define the best candidate.

The researchers found that the hydrogel could be easily injected using an off-the-shelf needle. Additionally, the gel started forming within minutes of mixing, ensuring sufficient time for the injection while minimizing spread at the injection site, so that the depot is small enough to be comfortable and inconspicuous.  In vitro  drug release assessments for all formulations showed extended and constant release rates over 1 to 3 months. The researchers also found that the release duration could be tailored through optimization of the hydrogel properties and loading.

The hydrogel-semaglutide formulation was also tested in six laboratory rats. In the rats, a single injection of the hydrogel-based therapy, showed limited burst (early release) and a regular release over a one-month period.

Importantly, the hydrogel was well tolerated with no inflammatory reaction over the treatment period. "Our pre-clinical results demonstrate that the regular, slow release of semaglutide over one month after administering a single dose, with limited early release, is achievable. Next we will be testing the hydrogel platform in pigs, whose skin and endocrine systems are most similar to those in humans. If that goes well, we will move forward the platform development by expecting clinical trials within the next few years," said Dr. Mégret.

Diabetologia

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

Tags: Agonist , Biotechnology , Diabetes , Drug Delivery , Drugs , Efficacy , Endocrine , GLP-1 , Glucagon , Glucagon-like Peptide-1 , Hormone , Hydrogel , in vitro , Laboratory , Obesity , Peptides , Polymers , Research , Semaglutide , Skin , Type 2 Diabetes , Weight Loss

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