Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

Performance of European prediction models for classification of type 1 and type 2 diabetes in Indians

AIM

We aimed to determine the performance of European prediction models in an Indian population to classify type 1 diabetes(T1D) and type 2 diabetes(T2D).

METHODS

We assessed discrimination and calibration of published models of diabetes classification, using retrospective data from electronic medical records of 83309 participants aged 18-50 years living in India. Diabetes type was defined based on C-peptide measurement and early insulin requirement. Models assessed combinations of clinical measurements: age at diagnosis, body mass index(mean = 26.6 kg/m2), sex(male = 64.9 %), Glutamic acid decarboxylase(GAD) antibody, serum cholesterol, serum triglycerides, and high-density lipoprotein(HDL) cholesterol.

RESULTS

67955 participants met inclusion criteria, of whom 0.8 % had T1D, which was markedly lower than model development cohorts. Model discrimination for clinical features was broadly similar in our Indian cohort compared to the European cohort: area under the receiver operating characteristic curve(AUC ROC) was 0.90 vs. 0.90 respectively, but was lower in the subset of young participants with measured GAD antibodies(n = 2404): and an AUC ROC of 0.87 when clinical features, sex, lipids and GAD antibodies were combined. All models substantially overestimated the likelihood of T1D, reflecting the lower prevalence of T1D in the Indian population. However, good model performance was achieved after recalibration by updating the model intercept and slope.

CONCLUSION

Models for diabetes classification maintain the discrimination of T1D and T2D in this Indian population, where T2D is far more common, but require recalibration to obtain appropriate model probabilities. External validation and recalibration are needed before these tools can be used in non-European populations.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

New advances in type 1 diabetes

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

2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes

CONTEXT

Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations.

OBJECTIVE

This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D.

METHODS

Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients.

RESULTS

The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D.

CONCLUSION

PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

The challenges of identifying and studying type 1 diabetes in adults

Diagnosing type 1 diabetes in adults is difficult since type 2 diabetes is the predominant diabetes type, particularly with an older age of onset (approximately >30 years). Misclassification of type 1 diabetes in adults is therefore common and will impact both individual patient management and the reported features of clinically classified cohorts. In this article, we discuss the challenges associated with correctly identifying adult-onset type 1 diabetes and the implications of these challenges for clinical practice and research. We discuss how many of the reported differences in the characteristics of autoimmune/type 1 diabetes with increasing age of diagnosis are likely explained by the inadvertent study of mixed populations with and without autoimmune aetiology diabetes. We show that when type 1 diabetes is defined by high-specificity methods, clinical presentation, islet-autoantibody positivity, genetic predisposition and progression of C-peptide loss remain broadly similar and severe at all ages and are unaffected by onset age within adults. Recent clinical guidance recommends routine islet-autoantibody testing when type 1 diabetes is clinically suspected or in the context of rapid progression to insulin therapy after a diagnosis of type 2 diabetes. In this moderate or high prior-probability setting, a positive islet-autoantibody test will usually confirm autoimmune aetiology (type 1 diabetes). We argue that islet-autoantibody testing of those with apparent type 2 diabetes should not be routinely undertaken as, in this low prior-prevalence setting, the positive predictive value of a single-positive islet antibody for autoimmune aetiology diabetes will be modest. When studying diabetes, extremely high-specificity approaches are needed to identify autoimmune diabetes in adults, with the optimal approach depending on the research question. We believe that until these recommendations are widely adopted by researchers, the true phenotype of late-onset type 1 diabetes will remain largely misunderstood.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

A Review on Latent Autoimmune Diabetes in Adults

Latent autoimmune diabetes (LADA) is an unique form of diabetes that has characteristics of both type 1 and type 2 diabetes. Type 1.5 diabetes also known as LADA is occasionally confused for type 2 diabetes because there is delay in presenting features and early insulin independence. LADA, on the other hand, is an autoimmune disorder that differs from type 2 diabetes in that autoantibodies against pancreatic beta cells are what characterise it. Insulin production eventually diminishes due to the autoimmune destruction of pancreatic beta cells as a result of the pathophysiology of LADA. Autoantibodies to glutamic acid decarboxylase (GAD), islet antigen-2 (IA-2), and insulin are frequently detected in LADA patients. These autoantibodies have important implications for therapy strategies and are essential in differentiating LADA from type 2 diabetes. LADA clinical management is very challenging. The aim of this article is to view the characteristics, disease presentation, diagnostic challenges, progression and treatment modalities of LADA.

Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study

Objective

With the high prevalence of pediatric obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D).

Methods

We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.75ng/ml) after >3 years (median 74 months) of diabetes duration. Models included clinical measures at baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL-C), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS).

Results

Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with c-peptide ≥0.75 ng/ml (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under receiver operator curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope=0.995-0.999). Models retained high performance for predicting retained c-peptide in older youth with obesity (AUCROC 0.88-0.96), and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92).

Conclusion

Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with type 2 diabetes.

Utility of genetic risk scores in type 1 diabetes

Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.

Validation of Register-Based Diabetes Classifiers in Danish Data

Purpose

To validate two register-based algorithms classifying type 1 (T1D) and type 2 diabetes (T2D) in a general population using Danish register data.

Patients and Methods

After linking data on prescription drug usage, hospital diagnoses, laboratory results and diabetes-specific healthcare services from nationwide healthcare registers, diabetes type was defined for all individuals in Central Denmark Region age 18-74 years on 31 December 2018 according to two distinct register-based classifiers: 1) a novel register-based diabetes classifier incorporating diagnostic hemoglobin-A1C measurements, the Open-Source Diabetes Classifier (OSDC), and 2) an existing Danish diabetes classifier, the Register for Selected Chronic Diseases (RSCD). These classifications were validated against self-reported data from the Health in Central Denmark survey - overall and stratified by age at onset of diabetes. The source-code of both classifiers was made available in the open-source R package osdc.

Results

A total of 2633 (9.0%) of 29,391 respondents reported having any type of diabetes, divided across 410 (1.4%) self-reported cases of T1D and 2223 (7.6%) cases of T2D. Among all self-reported diabetes cases, 2421 (91.9%) were classified as diabetes cases by both classifiers. In T1D, sensitivity of OSDC-classification was 0.773 [95% CI 0.730-0.813] (RSCD: 0.700 [0.653-0.744]) and positive predictive value (PPV) 0.943 [0.913-0.966] (RSCD: 0.944 [0.912-0.967]). In T2D, sensitivity of OSDC-classification was 0.944 [0.933-0.953] (RSCD: 0.905 [0.892-0.917]) and PPV 0.875 [0.861-0.888] (RSCD: 0.898 [0.884-0.910]). In age at onset-stratified analyses of both classifiers, sensitivity and PPV were low in individuals with T1D onset after age 40 and T2D onset before age 40.

Conclusion

Both register-based classifiers identified valid populations of T1D and T2D in a general population, but sensitivity was substantially higher in OSDC compared to RSCD. Register-classified diabetes type in cases with atypical age at onset of diabetes should be interpreted with caution. The validated, open-source classifiers provide robust and transparent tools for researchers.

Reclassification of type 2 diabetes to type 1 diabetes in Asturias (Spain) between 2011 and 2020

BACKGROUND

Differentiating between type 1 diabetes (T1D) and type 2 diabetes (T2D) can be difficult in adults. The aim of this study was to determine the frequency of diagnostic reclassification from T2D to T1D, the characteristics of the patients and the impact on the management of the disease.

METHODS

Observational and descriptive study including patients diagnosed with T1D in Asturias (Spain) between 2011 and 2020 who had been considered as T2D for at least 12 months.

RESULTS

A total of 205 patients were included, representing 45.3% of those diagnosed with T1D over 30 years of age. Median time of evolution as T2D was 7,8 years. The age was 59.1 ± 12.9 years. BMI was > 25 kg/m² in 46.8% of patients. HbA1c was 9.1 ± 2.1%, 77 ± 22 mmol/mol, and 56.5% were using insulin. Pancreatic antibodies were present in 95.5%, the most frequent being GAD, 82.6%. At 6 months, basal insulin use increased from 46.9 to 86.3%, and HbA1c decreased, 9.2 ± 2.0%vs7.7 ± 1.2%, 77 ± 22vs60 ± 13 mmol/mol; p < 0.0001.

CONCLUSIONS

Diagnosis as T2D in patients with T1D in adults is common. Age, BMI, insulin use and other clinical features are not definitely discriminatory. GAD is the antibody of choice in case of diagnostic suspect. Reclassification has important implications for metabolic control.

2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

OBJECTIVES

We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.

STUDY DESIGN AND SETTING

We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).

RESULTS

Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.

CONCLUSION

Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

Precision medicine in type 1 diabetes

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.

Adult-onset type 1 diabetes: A changing perspective

Type 1 diabetes most commonly presents in adulthood, contrary to the widely held view that it is a disease of childhood. Furthermore, a substantial proportion of cases of adult-onset type 1 diabetes does not require insulin therapy at clinical onset. Recent studies have emphasised the evidence that adult-onset type 1 diabetes is prevalent but often misclassified initially as type 2 diabetes (1, 2). In this review, we discuss that recent literature, highlighting the similarities and differences between adult-onset and childhood-onset type 1 diabetes, exploring recent debates surrounding its epidemiology and genetics, as well as expanding on important issues of diagnostic criteria for individuals presenting with adult-onset diabetes and the subsequent management once identified as having an autoimmune basis. In addition, this review looks at the psychosocial challenges faced by T1D patients and their possible management.

The longitudinal loss of islet autoantibody responses from diagnosis of type 1 diabetes occurs progressively over follow-up and is determined by low autoantibody titres, early-onset, and genetic variants

The clinical usefulness of post-diagnosis islet autoantibody levels is unclear and factors that drive autoantibody persistence are poorly defined in type 1 diabetes (T1D). Our aim was to characterise the longitudinal loss of islet autoantibody responses after diagnosis in a large, prospectively sampled UK cohort. Participants with T1D [n = 577] providing a diagnosis sample [range -1.0 to 2.0 years] and at least one post-diagnosis sample (<32.0 years) were tested for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A). Select HLA and non-HLA SNPs were considered. Non-genetic and genetic factors were assessed by multivariable logistic regression models for autoantibody positivity at initial sampling and autoantibody loss at final sampling. For GADA, IA-2A, and ZnT8A, 70.8%, 76.8%, and 40.1%, respectively, remained positive at the final sampling. Non-genetic predictors of autoantibody loss were low baseline autoantibody titres (P < 0.0001), longer diabetes duration (P < 0.0001), and age-at-onset under 8 years (P < 0.01--0.05). Adjusting for non-genetic covariates, GADA loss was associated with low-risk HLA class II genotypes (P = 0.005), and SNPs associated with autoimmunity RELA/11q13 (P = 0.017), LPP/3q28 (P = 0.004), and negatively with IFIH1/2q24 (P = 0.018). IA-2A loss was not associated with genetic factors independent of other covariates, while ZnT8A loss was associated with the presence of HLA A*24 (P = 0.019) and weakly negatively with RELA/11q13 (P = 0.049). The largest longitudinal study of islet autoantibody responses from diagnosis of T1D shows that autoantibody loss is heterogeneous and influenced by low titres at onset, longer duration, earlier age-at-onset, and genetic variants. These data may inform clinical trials where post-diagnosis participants are recruited.

C-peptide determination in the diagnosis of type of diabetes and its management: A clinical perspective

Impaired beta-cell function is a recognized cornerstone of diabetes pathophysiology. Estimates of insulin secretory capacity are useful to inform clinical practice, helping to classify types of diabetes, complication risk stratification and to guide treatment decisions. Because C-peptide secretion mirrors beta-cell function, it has emerged as a valuable clinical biomarker, mainly in autoimmune diabetes and especially in adult-onset diabetes. Nonetheless, the lack of robust evidence about the clinical utility of C-peptide measurement in type 2 diabetes, where insulin resistance is a major confounder, limits its use in such cases. Furthermore, problems remain in the standardization of the assay for C-peptide, raising concerns about comparability of measurements between different laboratories. To approach the heterogeneity and complexity of diabetes, reliable, simple and inexpensive clinical markers are required that can inform clinicians about probable pathophysiology and disease progression, and so enable personalization of management and therapy. This review summarizes the current evidence base about the potential value of C-peptide in the management of the two most prevalent forms of diabetes (type 2 diabetes and autoimmune diabetes) to address how its measurement may assist daily clinical practice and to highlight current limitations and areas of uncertainties to be covered by future research.

Type 1 diabetes in diverse ancestries and the use of genetic risk scores

Over 75 genetic loci within and outside of the HLA region influence type 1 diabetes risk. Genetic risk scores (GRS), which facilitate the integration of complex genetic information, have been developed in type 1 diabetes and incorporated into models and algorithms for classification, prognosis, and prediction of disease and response to preventive and therapeutic interventions. However, the development and validation of GRS across different ancestries is still emerging, as is knowledge on type 1 diabetes genetics in populations of diverse genetic ancestries. In this Review, we provide a summary of the current evidence on the evolutionary genetic variation in type 1 diabetes and the racial and ethnic differences in type 1 diabetes epidemiology, clinical characteristics, and preclinical course. We also discuss the influence of genetics on type 1 diabetes with differences across ancestries and the development and validation of GRS in various populations.

Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth

OBJECTIVE

Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S.

RESEARCH DESIGN AND METHODS

We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/-) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other).

RESULTS

T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA-/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA-/IS individuals had a greater probability of T1D than T2D. In DAA- individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency.

CONCLUSIONS

Diabetes type-specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA- children.

The Incidence of Adult-Onset Type 1 Diabetes: A Systematic Review From 32 Countries and Regions

BACKGROUND

The epidemiology of adult-onset type 1 diabetes (T1D) incidence is not well-characterized due to the historic focus on T1D as a childhood-onset disease.

PURPOSE

We assess the incidence of adult-onset (≥20 years) T1D, by country, from available data.

DATA SOURCES

A systematic review of MEDLINE, Embase, and the gray literature, through 11 May 2021, was undertaken.

STUDY SELECTION

We included all population-based studies reporting on adult-onset T1D incidence and published from 1990 onward in English.

DATA EXTRACTION

With the search we identified 1,374 references of which 46 were included for data extraction. Estimates of annual T1D incidence were allocated into broad age categories (20-39, 40-59, ≥60, or ≥20 years) as appropriate.

DATA SYNTHESIS

Overall, we observed the following patterns: 1) there is a paucity of data, particularly in low- and middle-income countries; 2) the incidence of adult-onset T1D is lowest in Asian and highest in Nordic countries; 3) adult-onset T1D is higher in men versus women; 4) it is unclear whether adult-onset T1D incidence declines with increasing age; and 5) it is unclear whether incidence of adult-onset T1D has changed over time.

LIMITATIONS

Results are generalizable to high-income countries, and misclassification of diabetes type cannot be ruled out.

CONCLUSIONS

From available data, this systematic review suggests that the incidence of T1D in adulthood is substantial and highlights the pressing need to better distinguish T1D from T2D in adults so that we may better assess and respond to the true burden of T1D in adults.

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) convened a writing group to develop a consensus statement on the management of type 1 diabetes in adults. The writing group has considered the rapid development of new treatments and technologies and addressed the following topics: diagnosis, aims of management, schedule of care, diabetes self-management education and support, glucose monitoring, insulin therapy, hypoglycaemia, behavioural considerations, psychosocial care, diabetic ketoacidosis, pancreas and islet transplantation, adjunctive therapies, special populations, inpatient management and future perspectives. Although we discuss the schedule for follow-up examinations and testing, we have not included the evaluation and treatment of the chronic microvascular and macrovascular complications of diabetes as these are well-reviewed and discussed elsewhere. The writing group was aware of both national and international guidance on type 1 diabetes and did not seek to replicate this but rather aimed to highlight the major areas that healthcare professionals should consider when managing adults with type 1 diabetes. Though evidence-based where possible, the recommendations in the report represent the consensus opinion of the authors. Graphical abstract.

The Management of Type 1 Diabetes in Adults. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) convened a writing group to develop a consensus statement on the management of type 1 diabetes in adults. The writing group has considered the rapid development of new treatments and technologies and addressed the following topics: diagnosis, aims of management, schedule of care, diabetes self-management education and support, glucose monitoring, insulin therapy, hypoglycemia, behavioral considerations, psychosocial care, diabetic ketoacidosis, pancreas and islet transplantation, adjunctive therapies, special populations, inpatient management, and future perspectives. Although we discuss the schedule for follow-up examinations and testing, we have not included the evaluation and treatment of the chronic microvascular and macrovascular complications of diabetes as these are well-reviewed and discussed elsewhere. The writing group was aware of both national and international guidance on type 1 diabetes and did not seek to replicate this but rather aimed to highlight the major areas that health care professionals should consider when managing adults with type 1 diabetes. Though evidence-based where possible, the recommendations in the report represent the consensus opinion of the authors.

Adult-Onset Type 1 Diabetes: Current Understanding and Challenges

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.

Impact of routine clinic measurement of serum C-peptide in people with a clinician-diagnosis of type 1 diabetes

AIMS/HYPOTHESIS

The aim of this study was to determine the impact of the routine use of serum C-peptide in an out-patient clinic setting on individuals with a clinician-diagnosis of type 1 diabetes.

METHODS

In this single-centre study, individuals with type 1 diabetes of at least 3 years duration were offered random serum C-peptide testing at routine clinic review. A C-peptide ≥200 pmol/L prompted further evaluation of the individual using a diagnostic algorithm that included measurement of islet cell antibodies and genetic testing. Where appropriate, a trial of anti-diabetic co-therapies was considered.

RESULTS

Serum C-peptide testing was performed in 859 individuals (90% of the eligible cohort), of whom 114 (13.2%) had C-peptide ≥200 pmol/L. The cause of diabetes was reclassified in 58 individuals (6.8% of the tested cohort). The majority of reclassifications were to type 2 diabetes (44 individuals; 5.1%), with a smaller proportion of monogenic diabetes (14 individuals; 1.6%). Overall, 13 individuals (1.5%) successfully discontinued insulin, while a further 16 individuals (1.9%) had improved glycaemic control following the addition of co-therapies. The estimated total cost of the testing programme was £23,262 (~€26,053), that is, £27 (~€30) per individual tested. In current terms, the cost of prior insulin therapy in the individuals with monogenic diabetes who successfully stopped insulin was approximately £57,000 (~€64,000).

CONCLUSIONS/INTERPRETATION

Serum C-peptide testing can easily be incorporated into an out-patient clinic setting and could be a cost-effective intervention. C-peptide testing should be strongly considered in individuals with a clinician-diagnosis of type 1 diabetes of at least 3 years duration.

The Genetics of Parkinson's Disease and Implications for Clinical Practice

The genetic landscape of Parkinson’s disease (PD) is characterised by rare high penetrance pathogenic variants causing familial disease, genetic risk factor variants driving PD risk in a significant minority in PD cases and high frequency, low penetrance variants, which contribute a small increase of the risk of developing sporadic PD. This knowledge has the potential to have a major impact in the clinical care of people with PD. We summarise these genetic influences and discuss the implications for therapeutics and clinical trial design.

Latent Autoimmune Diabetes of Adults (LADA) Is Likely to Represent a Mixed Population of Autoimmune (Type 1) and Nonautoimmune (Type 2) Diabetes

Latent autoimmune diabetes of adults (LADA) is typically defined as a new diabetes diagnosis after 35 years of age, presenting with clinical features of type 2 diabetes, in whom a type 1 diabetes-associated islet autoantibody is detected. Identifying autoimmune diabetes is important since the prognosis and optimal therapy differ. However, the existing LADA definition identifies a group with clinical and genetic features intermediate between typical type 1 and type 2 diabetes. It is unclear whether this is due to 1) true autoimmune diabetes with a milder phenotype at older onset ages that initially appears similar to type 2 diabetes but later requires insulin, 2) a disease syndrome where the pathophysiologies of type 1 and type 2 diabetes are both present in each patient, or 3) a heterogeneous group resulting from difficulties in classification. Herein, we suggest that difficulties in classification are a major component resulting from defining LADA using a diagnostic test-islet autoantibody measurement-with imperfect specificity applied in low-prevalence populations. This yields a heterogeneous group of true positives (autoimmune type 1 diabetes) and false positives (nonautoimmune type 2 diabetes). For clinicians, this means that islet autoantibody testing should not be undertaken in patients who do not have clinical features suggestive of autoimmune diabetes: in an adult without clinical features of type 1 diabetes, it is likely that a single positive antibody will represent a false-positive result. This is in contrast to patients with features suggestive of type 1 diabetes, where false-positive results will be rare. For researchers, this means that current definitions of LADA are not appropriate for the study of autoimmune diabetes in later life. Approaches that increase test specificity, or prior likelihood of autoimmune diabetes, are needed to avoid inclusion of participants who have nonautoimmune (type 2) diabetes. Improved classification will allow improved assignment of prognosis and therapy as well as an improved cohort in which to analyze and better understand the detailed pathophysiological components acting at onset and during disease progression in late-onset autoimmune diabetes.

A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

Background

Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults.

Methods

Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification variables included in the final model were based on their scores of importance. Model performance was evaluated by area under the receiver operating characteristic curve (ROC AUC), sensitivity, and specificity. The performance of models with different variable combinations was compared. Calibration intercept and slope were evaluated for the final model.

Results

Among the newly diagnosed diabetes cohort, 1,465 (9.63%) persons had T1DM and 13,741 (90.37%) had T2DM. Body mass index (BMI) contributed the most to the model, followed by age of onset and hemoglobin A1c (HbA1c). Compared with models with other clinical variable combinations, a final model that integrated age of onset, BMI and HbA1c had relatively higher performance. The ROC AUC, sensitivity, and specificity for this model were 0.83 (95% CI, 0.80 to 0.85), 0.77, and 0.76, respectively. The calibration intercept and slope were 0.02 (95% CI, –0.03 to 0.06) and 0.90 (95% CI, 0.79 to 1.02), respectively, which suggested a good calibration performance.

Conclusions

Our classification model that integrated age of onset, BMI, and HbA1c could distinguish T1DM from T2DM, which provides a useful tool in assisting physicians in subtyping and precising treatment in diabetes.

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes

AIMS

Misclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes.

METHODS

We classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n = 111) or non-type 1 (n = 42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC).

RESULTS

Diagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P = 0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median < 17 pmol/l (limit of detection) vs. 1037 pmol/l in non-type 1 diabetes; P < 0.0001].

CONCLUSIONS

Our study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment

Despite the known heterogeneity of type 2 diabetes and variable response to glucose lowering medications, current evidence on optimal treatment is predominantly based on average effects in clinical trials rather than individual-level characteristics. A precision medicine approach based on treatment response would aim to improve on this by identifying predictors of differential drug response for people based on their characteristics and then using this information to select optimal treatment. Recent research has demonstrated robust and clinically relevant differential drug response with all noninsulin treatments after metformin (sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide 1 [GLP-1] receptor agonists, and sodium-glucose cotransporter 2 [SGLT2] inhibitors) using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and testing precision medicine-based strategies to optimize treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that "subtype" approaches, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with approaches that combine the same features as continuous measures in probabilistic "individualized prediction" models.

Management of Latent Autoimmune Diabetes in Adults: A Consensus Statement From an International Expert Panel

A substantial proportion of patients with adult-onset diabetes share features of both type 1 diabetes (T1D) and type 2 diabetes (T2D). These individuals, at diagnosis, clinically resemble T2D patients by not requiring insulin treatment, yet they have immunogenetic markers associated with T1D. Such a slowly evolving form of autoimmune diabetes, described as latent autoimmune diabetes of adults (LADA), accounts for 2-12% of all patients with adult-onset diabetes, though they show considerable variability according to their demographics and mode of ascertainment. While therapeutic strategies aim for metabolic control and preservation of residual insulin secretory capacity, endotype heterogeneity within LADA implies a personalized approach to treatment. Faced with a paucity of large-scale clinical trials in LADA, an expert panel reviewed data and delineated one therapeutic approach. Building on the 2020 American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) consensus for T2D and heterogeneity within autoimmune diabetes, we propose "deviations" for LADA from those guidelines. Within LADA, C-peptide values, proxy for β-cell function, drive therapeutic decisions. Three broad categories of random C-peptide levels were introduced by the panel: 1) C-peptide levels <0.3 nmol/L: a multiple-insulin regimen recommended as for T1D; 2) C-peptide values ≥0.3 and ≤0.7 nmol/L: defined by the panel as a "gray area" in which a modified ADA/EASD algorithm for T2D is recommended; consider insulin in combination with other therapies to modulate β-cell failure and limit diabetic complications; 3) C-peptide values >0.7 nmol/L: suggests a modified ADA/EASD algorithm as for T2D but allowing for the potentially progressive nature of LADA by monitoring C-peptide to adjust treatment. The panel concluded by advising general screening for LADA in newly diagnosed non-insulin-requiring diabetes and, importantly, that large randomized clinical trials are warranted.

Allostasis and the origins of adult-onset diabetes

Physiological plasticity enables homeostasis to be maintained in biological systems, but when such allostasis fails, then disease can develop. In a new population-based study by Rolandsson et al (https://doi.org/10.1007/s00125-019-05016-3), autoimmunity, defined by an immunogenotype, predicted adult-onset non-insulin requiring diabetes. Type 1 diabetes is no longer viewed as a disease confined to children, with a significant proportion, maybe the majority, presenting in adulthood. Such cases masquerade as type 2 diabetes and their identification has clinical utility. Nevertheless, in this study, autoimmunity had a limited effect on the overall risk of adults developing diabetes.

Latent Autoimmune Diabetes in Adults: A Review of Clinically Relevant Issues

Latent autoimmune diabetes in adults (LADA) is still a poorly characterized entity. However, its prevalence may be higher than that of classical type 1 diabetes. Patients with LADA are often misclassified as type 2 diabetes. The underlying autoimmune process against β-cell has important consequences for the prognosis, comorbidities, treatment choices and even patient-reported outcomes with this diabetes subtype. However, there is still an important gap of knowledge in many areas of clinical relevance. We are herein focusing on the state of knowledge of relevant clinical issues than may help in the diagnosis and management of subjects with LADA.