Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease

Rising tide: The global surge of type 2 diabetes in children and adolescents demands action now

Childhood-onset obesity has emerged as a major public healthcare challenge across the globe, fueled by an obesogenic environment and influenced by both genetic and epigenetic predispositions. This has led to an exponential rise in the incidence of type 2 diabetes mellitus in children and adolescents. The looming wave of diabetes-related complications in early adulthood is anticipated to strain the healthcare budgets in most countries. Unless there is a collective global effort to curb the devastation caused by the situation, the impact is poised to be pro-found. A multifaceted research effort, governmental legislation, and effective social action are crucial in attaining this goal. This article delves into the current epidemiological landscape, explores evidence concerning potential risks and consequences, delves into the pathobiology of childhood obesity, and discusses the latest evidence-based management strategies for diabesity.

Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR] = 11.4 [6.2] years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥ 3 years post-diabetes diagnosis (n = 1019), we compared youth with steady versus reclassified diabetes type. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p < 0.05). In the longitudinal subcohort, diabetes type was reclassified in 35 youth (3.4%); this was not statistically associated with any single characteristic. In sum, among racially/ethnically diverse youth with diabetes, 6.4% had inaccurate diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type.

Enhancing outcome prediction by applying the 2019 WHO DM classification to adults with hyperglycemic crises: A single-center cohort in Thailand

BACKGROUND AND AIMS

Hyperglycemic crisis is a metabolic catastrophe which can occur in any type of diabetes. In 2019, the World Health Organization (WHO) revised the classification of diabetes mellitus (DM) and established two new hybrid forms, latent autoimmune diabetes in adults (LADA) and ketosis-prone type 2 diabetes (T2D). This study aimed to determine clinical outcomes after a hyperglycemic crisis event in people with diabetes classified subtypes by 2019 WHO DM classification.

METHODS

A five-year (2015-2019) retrospective study of adult patients admitted with hyperglycemic crises was conducted. Types of diabetes were recategorized based on the 2019 WHO DM classification. Clinical characteristics, in-admission treatment and complications, long-term follow-up outcomes, and mortality were collected, analyzed, and compared.

RESULTS

A total of 185 admissions occurred in 136 patients. The mean age was 50.6 ± 18.4 years (49.3 % men). The annual average incidence of hyperglycemic crises was 5.2 events/1000 persons. The proportion of type 1 diabetes, T2D, LADA, ketosis-prone T2D, and pancreatic DM were 15.4 %, 69.1 %, 2.2 %, 11 %, and 2.2 %, respectively. In-hospital mortality was 3.7 % while cumulative mortality totaled 19.1 %. During the 24-month follow-up, ketosis-prone T2D had the highest success of insulin discontinuation (HR 6.59; 95 % CI 6.69-319.4; p < 0.001), while T2D demonstrated the highest mortality compared to others (HR, 2.89; 95%CI 1.15-6.27; p = 0.02).

CONCLUSION

The reclassification of diabetes based on 2019 WHO DM classification helped elucidate differences in long-term outcomes and mortality among DM types. The new classification, which separates ketosis-prone T2D from standard T2D, should be encouraged in clinical practice for precise and individualized management.

GAD65Abs Are Not Associated With Beta-Cell Dysfunction in Patients With T2D in the GRADE Study

Context

Autoantibodies directed against the 65-kilodalton isoform of glutamic acid decarboxylase (GAD65Abs) are markers of autoimmune type 1 diabetes (T1D) but are also present in patients with Latent Autoimmune Diabetes of Adults and autoimmune neuromuscular diseases, and also in healthy individuals. Phenotypic differences between these conditions are reflected in epitope-specific GAD65Abs and anti-idiotypic antibodies (anti-Id) against GAD65Abs. We previously reported that 7.8% of T2D patients in the GRADE study have GAD65Abs but found that GAD65Ab positivity was not correlated with beta-cell function, glycated hemoglobin (HbA1c), or fasting glucose levels.

Context

In this study, we aimed to better characterize islet autoantibodies in this T2D cohort. This is an ancillary study to NCT01794143.

Methods

We stringently defined GAD65Ab positivity with a competition assay, analyzed GAD65Ab-specific epitopes, and measured GAD65Ab-specific anti-Id in serum.

Results

Competition assays confirmed that 5.9% of the patients were GAD65Ab positive, but beta-cell function was not associated with GAD65Ab positivity, GAD65Ab epitope specificity or GAD65Ab-specific anti-Id. GAD65-related autoantibody responses in GRADE T2D patients resemble profiles in healthy individuals (low GAD65Ab titers, presence of a single autoantibody, lack of a distinct epitope pattern, and presence of anti-Id to diabetes-associated GAD65Ab). In this T2D cohort, GAD65Ab positivity is likely unrelated to the pathogenesis of beta-cell dysfunction.

Conclusion

Evidence for islet autoimmunity in the pathophysiology of T2D beta-cell dysfunction is growing, but T1D-associated autoantibodies may not accurately reflect the nature of their autoimmune process.

Development and validation of a multivariable risk prediction model for identifying ketosis-prone type 2 diabetes

BACKGROUND

To develop and validate a multivariable risk prediction model for ketosis-prone type 2 diabetes mellitus (T2DM) based on clinical characteristics.

METHODS

A total of 964 participants newly diagnosed with T2DM were enrolled in the modeling and validation cohort. Baseline clinical data were collected and analyzed. Multivariable logistic regression analysis was performed to select independent risk factors, develop the prediction model, and construct the nomogram. The model's reliability and validity were checked using the receiver operating characteristic curve and the calibration curve.

RESULTS

A high morbidity of ketosis-prone T2DM was observed (20.2%), who presented as lower age and fasting C-peptide, and higher free fatty acids, glycated hemoglobin A1c and urinary protein. Based on these five independent influence factors, we developed a risk prediction model for ketosis-prone T2DM and constructed the nomogram. Areas under the curve of the modeling and validation cohorts were 0.806 (95% confidence interval [CI]: 0.760-0.851) and 0.856 (95% CI: 0.803-0.908). The calibration curves that were both internally and externally checked indicated that the projected results were reasonably close to the actual values.

CONCLUSIONS

Our study provided an effective clinical risk prediction model for ketosis-prone T2DM, which could help for precise classification and management.

Imprecise Diagnosis of Diabetes Type in Youth: Prevalence, Characteristics, and Implications

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR]=11.4 [6.2] years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥3 years post-diabetes diagnosis (n=1019), we compared youth with unchanged versus changed diabetes classification. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p<0.05). In the longitudinal subcohort, diabetes classification changed in 35 youth (3.4%); this was not statistically associated with any single characteristic. Having unknown or revised diabetes type was associated with less continuous glucose monitor use on follow-up (both, p<0.004). In sum, among racially/ethnically diverse youth with diabetes, 6.5% had imprecise diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type.

Remission in Ketosis-Prone Diabetes

Heterogeneous forms of Ketosis-prone diabetes (KPD) are characterized by patients who present with diabetic ketoacidosis (DKA) but lack the typical features and biomarkers of autoimmune T1D. The A-β+ subgroup of KPD provides unique insight into the concept of "remission" since these patients have substantial preservation of beta-cell function permitting the discontinuation of insulin therapy, despite initial presentation with DKA. Measurements of C-peptide levels are essential to predict remission and guide potential insulin withdrawal. Further studies into predictors of remission and relapse can help us guide patients with A-β+ KPD toward remission and develop targeted treatments for this form of atypical diabetes.

Algorithmic identification of atypical diabetes in electronic health record (EHR) systems

AIMS

Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.

METHODS

Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information.

RESULTS

Of 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01).

CONCLUSION

Our EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

The current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future.

Spectrum of Phenotypes and Causes of Type 2 Diabetes in Children

Several factors, including genetics, family history, diet, physical activity, obesity, and insulin resistance in puberty, appear to increase the risk of type 2 diabetes in youth. Youth-onset type 2 diabetes is often thought of as a single entity but rather exists as a spectrum of disease with differences in presentation, metabolic characteristics, clinical progression, and complication rates. We review what is currently known regarding the risks associated with developing type 2 diabetes in youth. Additionally, we focus on the spectrum of phenotypes of pediatric type 2 diabetes, discuss the pathogenic underpinnings and potential therapeutic relevance of this heterogeneity, and compare youth-onset type 2 diabetes with type 1 diabetes and adult-onset type 2 diabetes. Finally, we highlight knowledge gaps in prediction and prevention of youth-onset type 2 diabetes.

HNF1A：From Monogenic Diabetes to Type 2 Diabetes and Gestational Diabetes Mellitus

Diabetes, a disease characterized by hyperglycemia, has a serious impact on the lives and families of patients as well as on society. Diabetes is a group of highly heterogeneous metabolic diseases that can be classified as type 1 diabetes (T1D), type 2 diabetes (T2D), gestational diabetes mellitus (GDM), or other according to the etiology. The clinical manifestations are more or less similar among the different types of diabetes, and each type is highly heterogeneous due to different pathogenic factors. Therefore, distinguishing between various types of diabetes and defining their subtypes are major challenges hindering the precise treatment of the disease. T2D is the main type of diabetes in humans as well as the most heterogeneous. Fortunately, some studies have shown that variants of certain genes involved in monogenic diabetes also increase the risk of T2D. We hope this finding will enable breakthroughs regarding the pathogenesis of T2D and facilitate personalized treatment of the disease by exploring the function of the signal genes involved. Hepatocyte nuclear factor 1 homeobox A (HNF1α) is widely expressed in pancreatic β cells, the liver, the intestines, and other organs. HNF1α is highly polymorphic, but lacks a mutation hot spot. Mutations can be found at any site of the gene. Some single nucleotide polymorphisms (SNPs) cause maturity-onset diabetes of the young type 3 (MODY3) while some others do not cause MODY3 but increase the susceptibility to T2D or GDM. The phenotypes of MODY3 caused by different SNPs also differ. MODY3 is among the most common types of MODY, which is a form of monogenic diabetes mellitus caused by a single gene mutation. Both T2D and GDM are multifactorial diseases caused by both genetic and environmental factors. Different types of diabetes mellitus have different clinical phenotypes and treatments. This review focuses on HNF1α gene polymorphisms, HNF1A-MODY3, HNF1A-associated T2D and GDM, and the related pathogenesis and treatment methods. We hope this review will provide a valuable reference for the precise and individualized treatment of diabetes caused by abnormal HNF1α by summarizing the clinical heterogeneity of blood glucose abnormalities caused by HNF1α mutation.