HOMA2-B enhances assessment of type 1 diabetes risk among TrialNet Pathway to Prevention participants

Time to reframe the disease staging system for type 1 diabetes

In 2015, introduction of a disease staging system offered a framework for benchmarking progression to clinical type 1 diabetes. This model, based on islet autoantibodies (stage 1) and dysglycaemia (stage 2) before type 1 diabetes diagnosis (stage 3), has facilitated screening and identification of people at risk. Yet, there are many limitations to this model as the stages combine a very heterogeneous group of individuals; do not have high specificity for type 1 diabetes; can occur without persistence (ie, reversion to an earlier risk stage); and exclude age and other influential risk factors. The current staging system also infers that individuals at risk of type 1 diabetes progress linearly from stage 1 to stage 2 and subsequently stage 3, whereas such movements are often more complex. With the approval of teplizumab by the US Food and Drug Administration in 2022 to delay type 1 diabetes in people at stage 2, there is a need to refine the definition and accuracy of type 1 diabetes staging. Theoretically, we propose that a type 1 diabetes risk calculator should incorporate any available demographic, genetic, autoantibody, metabolic, and immune data that could be continuously updated. Additionally, we call to action for the field to increase the breadth of knowledge regarding type 1 diabetes risk in non-relatives, adults, and individuals from minority populations.

Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes

BACKGROUND

Essentially all individuals with multiple autoantibodies will develop clinical type 1 diabetes. Multiple autoantibodies (AABs) and normal glucose tolerance define stage 1 diabetes; abnormal glucose tolerance defines stage 2. However, the rate of progression within these stages is heterogeneous, necessitating personalized risk calculators to improve clinical implementation.

METHODS

We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The small model included AAB status, fasting glucose, hemoglobin A1c, and age, while the medium and large models added predictors of disease progression measured via oral glucose tolerance testing.

FINDINGS

All models markedly improved granularity regarding personalized risk missing from current categories of stages of type 1 diabetes. Model-derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; a lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of the rate of disease progression within stage 2.

CONCLUSION

While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.

Glycated haemoglobin, HOMA2-B, C-peptide to glucose ratio and type 2 diabetes clusters as predictors for therapy failure in individuals with type 2 diabetes without insulin therapy: A registry analysis

AIM

Some people with type 2 diabetes mellitus (T2D) and declining β-cell function do require insulin over time. Various laboratory parameters, indices of glucose metabolism or phenotypes of T2D (clusters) have been suggested, which might predict future therapy failure (TF), indicating the need for insulin therapy initiation. This analysis evaluated glycated haemoglobin (HbA1c), homeostatic model assessment (HOMA)2-B, C-peptide to glucose ratio (CGR) and diabetes clusters as predictive parameters for the occurrence of glycaemic TF in individuals diagnosed with T2D without previous insulin therapy.

MATERIALS AND METHODS

In total, 159 individuals with T2D [41% female, median age 50 (IQR: 53-69) years, diabetes duration 9 (5-15) years], without insulin therapy were prospectively evaluated for the occurrence of a composite primary endpoint, including HbA1c increasing or remaining >8.0% (64 mmol/mol) 3 months after baseline on non-insulin glucose-lowering agents, insulin initiation or hospital admissions because of acute hyperglycaemic events. Diabetes clusters were formed according to previously described characteristics. Only severe autoimmune diabetes clusters were excluded because of a small amount of glutamate decarboxylase antibody-positive participants. The other clusters were distributed as mild age-related diabetes 33%; severe insulin-deficient diabetes 31%; mild obesity-related diabetes 20%; and severe insulin-resistant diabetes 15%.

RESULTS

During a median observation of 57 months, higher tertiles of HbA1c at baseline, HOMA2-B, as well as a lower CGR were significantly predictive for the occurrence of the primary endpoint. The probability of meeting the primary endpoint was the highest for mild obesity-related diabetes [hazard ratio 3.28 (95% confidence interval 1.75-6.2)], followed by severe insulin-deficient diabetes [hazard ratio 2.03 (95% confidence interval 1.1-3.7)], mild age-related diabetes and the lowest for severe insulin-resistant diabetes. The best performance to predict TF with an area under the curve (AUC) of 0.77 was HbA1c at baseline, followed by HOMA2-B (AUC 0.69) and CGR (AUC 0.64).

CONCLUSION

HbA1c, indices of insulin secretion capacity (HOMA2-B and CGR) and T2D clusters might be applicable tools to guide practitioners in the decision of whether insulin is required in people already diagnosed with T2D. These findings need to be validated in prospective studies.

Family History of Type 2 Diabetes and Its Association with Beta Cell Function and Lipid Profile in Newly Diagnosed Pediatric Patients with Type 1 Diabetes

OBJECTIVE

The objective of this study was to explore the associations between a family history of type 2 diabetes (T2D) and beta-cell function, as well as lipid profile, in pediatric patients newly diagnosed with type 1 diabetes (T1D).

METHODS

A retrospective analysis was conducted on children under 14 years of age who were newly diagnosed with T1D at the Children's Hospital of Zhejiang University between August 2018 and August 2022. Clinical features, metabolic profiles, beta-cell function, and lipid profile were evaluated.

RESULTS

A total of 316 children were diagnosed with new-onset T1D. Among them, 28.2% had a family history of T2D. Patients with T1D who had a family history of T2D experienced a later onset of the disease (p = 0.016), improved HOMA2-%B levels (p = 0.003), and increased concentrations of HDL-C (p = 0.005). In addition, no statistically significant differences in age at onset, HOMA2-%B levels, or HDL-C were found when assessing the interaction between family history of T2D and type of diabetes mellitus (autoimmune T1D/idiopathic T1D).

CONCLUSION

A family history of T2D may contribute to the heterogeneity of T1D patients in terms of HOMA2-%B levels and lipid profile. This highlights the significance of taking into account T2D-related factors in the diagnosis and treatment of T1D.

Medication Deprescribing Among Patients With Type 2 Diabetes: A Qualitative Case Series of Lifestyle Medicine Practitioner Protocols

This study is a qualitative case series of lifestyle medicine practitioners' protocols for medication de-escalation in the context of reduced need for glucose-lowering medications due to lifestyle modifications. Increasing numbers of lifestyle medicine practitioners report achieving reductions in medications among patients with type 2 diabetes, and in some cases remission, but limited data exist on the clinical decision-making process used to determine when and how medications are deprescribed. Practitioners interviewed here provide accounts of their deprescribing protocols. This information can serve as pilot data for other practitioners seeking examples of how deprescribing in the context of lifestyle medicine treatment is conducted.