Production of insulin antibodies by mice rejecting insulin transfected cells

The application of predictive value of diabetes autoantibody profile combined with clinical data and routine laboratory indexes in the classification of diabetes mellitus

Objective

Currently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus.

Methods

Clinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Results

A total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically.

Conclusion

The established prediction model combining patient's age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.

Autoantibodies in type 1 diabetes

Diabetes mellitus is a disorder characterized by hyperglycemia in both the fasting and post-prandial states. The two most common forms of diabetes mellitus, type 1 and type 2 (previously called juvenile-onset and adult-onset, respectively), comprise the vast majority of cases. Type 1 diabetes (T1DM) has been shown to be a disease characterized by immune-mediated destruction of the insulin-secreting cells of the pancreas; it comprises the majority of cases of diabetes seen in childhood and approximately, 5-10% of all cases of diabetes mellitus in the USA and perhaps accounts for an even higher percentage in those nations with lower rates of obesity. The process of beta-cell destruction, marked by the production of autoantibodies to the beta-cell, occurs over many years and ultimately results in metabolic abnormalities first manifested as impaired glucose tolerance and then progressing to symptomatic hyperglycemia. It has been reported that approximately 50% of the genetic risk for T1DM can be attributed to the HLA region. The highest risk HLA-DR3/4 DQ8 genotype has been shown to be highly associated with beta-cell autoimmunity. The first antibodies described in association with the development of T1DM were islet cell autoantibodies (ICA). Subsequently, antibodies to insulin (IAA), glutamic acid decarboxylase (GAA or GAD) and protein tyrosine phosphatase (IA2 or ICA512) have all been defined. The number of antibodies, rather than the individual antibody, is thought to be most predictive of progression to overt diabetes.