Practical Clinical Applications of Islet Autoantibody Testing in Type 1 Diabetes

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.

Estimation of Individual Positive Anti-Islet Autoantibodies from 3 Screen ICA Titer

The 3 Screen ICA ELISA is a novel assay capable of simultaneously measuring autoantibodies to glutamic acid decarboxylase (GADA), insulinoma-associated antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A), making it a valuable tool for screening type 1 diabetes. Despite its advantages, it cannot specify which individual autoantibodies are positive or negative. This study aimed to estimate individual positive autoantibodies based on the 3 Screen ICA titer. Six hundred seventeen patients with type 1 diabetes, simultaneously measured for 3 Screen ICA and three individual autoantibodies, were divided into five groups based on their 3 Screen ICA titer. The sensitivities and contribution rates of the individual autoantibodies were then examined. The study had a cross-sectional design. Sixty-nine percent (424 of 617) of patients with type 1 diabetes had 3 Screen ICA titers exceeding the 99th percentile cut-off level (20 index). The prevalence of GADA ranged from 80% to 100% in patients with a 3 Screen ICA over 30 index and 97% of patients with a 3 Screen ICA ≥300 index. Furthermore, the prevalence of all individual autoantibodies being positive was 0% for ≤80 index and as high as 92% for ≥300 index. Significant associations were observed in specific titer groups: the 20-29.9 index group when all the individual autoantibodies were negative, the 30-79.9 index group when positive for GADA alone or IA-2A alone, the 30-299.9 index group when positive for ZnT8A alone, the 80-299.9 index group when positive for both IA-2A and ZnT8A, the 300-499.9 index group when positive for both GADA and ZnT8A, and the ≥300 index group when positive for all individual autoantibodies. These results suggest that the 3 Screen ICA titer may be helpful in estimating individual positive autoantibodies.

Correlation analysis between cytokines' profile, autoimmune antibodies and the duration of type 1 diabetes: A case control study in a specialized children's centre in Riyadh

OBJECTIVE

The aim of this study was to investigate the role of cytokines in children with T1D living in Saudi Arabia and their correlation with disease duration and autoimmune antibody markers.

METHODS

A case-control study was conducted in the endocrine clinic of King Abdullah Specialized Children's Hospital in Riyadh. A total of 274 T1D and healthy control children were enrolled in the study. 5 mL of venous blood samples were collected in the morning after 9 to 12 h of fasting in BD Vacutainer® EDTA tubes and centrifuged at 250g for 15 min at. Plasma was then stored at -20°C for detection of anti-islet, anti-GAD antibodies (Abs), and C-peptide using commercial ELISA kits from Thermo Fisher Scientific. The levels of cytokines were measured using commercial sandwich ELISA kits from Abcam.

RESULTS

Median differences in cytokine levels (IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, IL-35, and IL-37) were significantly higher in T1D patients compared with healthy controls (p-value < .001). Spearman's Rho correlation indicated that TNFα, IL-1β, IL-4, IL-10, IL-13, and IL-21 correlated significantly with T1D Abs (p-value = .01). HbA1C correlated negatively with IL-35 and IL-37, and positively with IL-18 (p-value = .01). Linear regression analysis showed a significant increase in anti-glutamic acid antibodies (GAD) in patients with >3 years of T1D duration.

CONCLUSION

Autoantibodies remained positive at high levels in our patients over a 3-year duration of the disease and correlated with specific cytokines. The clear correlations with disease duration and profile of specific cytokines could be targets for future therapeutic interventions.

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.

Antigen-specific immunotherapies in type 1 diabetes

Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the destruction of pancreatic beta cells, in which immune system disorder plays an important role. Finding a cure for T1DM and restoring beta cell function has been a long-standing goal. Research has shown that immune regulation with pancreatic islet auto-antigens may be the most specific and safe treatment for T1DM. Immunological intervention using diabetogenic auto-antigens as a target can help identify T1DM in high-risk individuals by early screening of autoantibodies (AAbs) before the loss of pancreatic islet function and thus achieve primary prevention of T1DM. However, induction of self-tolerance in patients with pre-diabetes can also slow down the attack of autoimmunity, and achieve secondary prevention. Antigen-based immune therapy opens up new avenues for the prevention and treatment of T1DM. The zinc transporter 8 (ZnT8) protein, presents in the serum of pre-diabetic and diabetic patients, is immunogenic and can cause T1D autoimmune responses. ZnT8 has become a potential target of humoral autoimmunity; it is of great significance for the early diagnosis of T1D. ZnT8-specific CD8⁺ T cells can be detected in most T1DM patients, and play a key role in the progression of T1D. As an immunotherapy target, it can improve the dysfunction of beta cells in T1DM and provide new ideas for the treatment of T1D. In this review, we summarize research surrounding antigen-specific immunotherapies (ASI) over the past 10 years and the ZnT8 antigen as an autoimmune target to induce self-tolerance for T1DM.