Trends in endogenous insulin secretion capacity and anti-islet autoantibody titers in two childhood-onset slowly progressive insulin-dependent diabetes mellitus cases.

Slowly progressive insulin-dependent (type 1) diabetes mellitus (SPIDDM) is a subtype of type 1 diabetes. Although SPIDDM is not rare among Japanese children, there are few reports on endogenous insulin secretory capacity and anti-pancreatic islet autoantibodies in pediatric SPIDDM. We followed the trends in endogenous insulin secretory capacity and anti-pancreatic islet autoantibody titers in two pediatric SPIDDM cases over several years. Case 1 developed insulin deficiency eight months after diabetes diagnosis; as her insulinoma-associated antibody test result was positive, insulin therapy was initiated. Fourteen months after the diagnosis, she tested positive for glutamic acid decarboxylase autoantibodies (GADA) and was diagnosed with SPIDDM. Case 2 was mildly positive for GADA at the onset of diabetes, but became a high titer during the course of the disease. Fourteen months after the diagnosis of diabetes, he became mildly insulin deficient, and insulin therapy was initiated. However, his insulin secretory capacity was preserved for 60 mo after the onset. SPIDDM is generally indistinguishable from type 2 diabetes at diagnosis; therefore, repeated evaluation of the insulin secretory capacity and anti-islet autoantibodies facilitates early diagnosis and appropriate treatment, especially in nonobese children with type 2 diabetes.

Clinical Features and HLA Genetics Differ in Children at Type 1 Diabetes Onset by Hispanic Ethnicity.

CONTEXT: Type 1 diabetes incidence continues to increase in children, especially among Hispanic Whites (HW). OBJECTIVE: We investigated the clinical, immunologic, and genetic characteristics of HW and Non-Hispanic White (NHW) children that presented at type 1 diabetes diagnosis. METHODS: In this single-center, observational study, children who were diagnosed with type 1 diabetes (<20 years old) and tested for islet autoantibodies within 1 year of diagnosis were included in the study and divided into two groups by Hispanic ethnicity. RESULTS: Of 1297 children, 398 HW children presented with a younger age at diabetes onset (10.2 ± 3.9 vs. 11.1 ± 4.1 years, p<0.001) and more diabetic ketoacidosis (62.4% vs. 51.9%, p<0.001) compared to NHW children (n=899). There was no difference in sex, A1c levels, or the number and prevalence of islet autoantibodies between the two cohorts. A subset of our cohort was HLA typed as specific alleles confer strong genetic risk for type 1 diabetes (e.g., HLA-DR4 and DQ8). Among 637 HLA-typed children, HW children had a significantly higher prevalence of the DR4-DQ8 haplotype compared to NHW children (79.1% vs. 60.1%, p<0.001), and this frequency was much higher than a reference Hispanic population (OR = 6.5, 95% CI 4.6-9.3). CONCLUSIONS: Hispanic White children developing type 1 diabetes have a high prevalence of HLA DR4-DQ8, which can be utilized to select individuals for immune monitoring with islet autoantibodies to lessen diabetic ketoacidosis and potentially prevent diabetes onset.

Islet autoantibody frequency in relatives of children with type 1 diabetes who have a type 2 diabetes diagnosis.

AIM: This study aimed to evaluate characteristics of autoimmunity in individuals who have a type 2 diagnosis and are relatives of children with type 1 diabetes. METHODS: Pre-diagnosis samples (median 17 months before onset) from relatives who were later diagnosed with type 2 diabetes were measured for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), zinc transporter 8 (ZnT8A) and insulin (IAA) as well as the type 1 diabetes genetic risk score (GRS2). Associations between islet autoantibodies, insulin treatment and GRS2 were analysed using Fisher's exact and t-tests. RESULTS: Among 226 relatives (64% men; mean age at sampling 41 years; mean age 54 years at diagnosis), 32 (14%) were islet autoantibody-positive for at least one autoantibody more than a decade before diagnosis. Approximately half of these (n = 15) were treated with insulin. GADA-positivity was higher in insulin-treated relatives than in non-insulin-treated relatives (12/18 [67%] vs. 6/18 [33%], p < 0.001). IAA-positivity was observed in 13/32 (41%) of relatives with autoantibodies. GRS2 scores were increased in autoantibody-positive relatives (p = 0.032), but there was no clear evidence for a difference according to treatment (p = 0.072). CONCLUSION: This study highlights the importance of measuring islet autoantibodies, including IAA, in relatives of people with type 1 diabetes to avoid misdiagnosis.

Enzyme-linked immunosorbent assay of 3 Screen Islet Cell Autoantibody in patients with autoimmune thyroid disease.

BACKGROUND: In recent years, the emergence of multiplex technology that can simultaneously measure multiple anti-islet autoantibodies has become particularly valuable for the staging and early diagnosis of immune-mediated type 1 diabetes (T1D). While it has been established that 20%-30% of T1D patients suffer from autoimmune thyroid disease (AITD), there is limited available data regarding the presence of anti-islet autoantibodies in AITD patients. Among commercially available anti-islet autoantibodies, glutamic acid decarboxylase 65 autoantibodies (GADAs) are often the first marker measured in general clinical practice. AIM: To investigate the frequency of anti-islet autoantibodies in AITD patients. METHODS: Our study involved four hundred ninety-five AITD patients, categorized into three distinct groups: AITD with T1D (n = 18), AITD with phenotypic type 2 diabetes (T2D) (n = 81), and AITD without diabetes (n = 396), and the enzyme-linked immunosorbent assay (ELISA) was employed to determine the frequencies of 3 Screen Islet Cell Autoantibody (3 Screen ICA), GADA, insulinoma-associated antigen-2 autoantibodies (IA-2As), and zinc transporter 8 autoantibodies (ZnT8As) within these groups. RESULTS: The frequency of 3 Screen ICA in AITD patients with T1D, T2D, and those without diabetes were 88.9%, 6.2%, and 5.1%, respectively, with no significant difference seen between the latter two groups. Notably, the frequency of 3 Screen ICA was 11.1% higher in AITD patients with T1D, 1.3% higher in AITD patients with T2D, and 1.1% higher in AITD patients without diabetes compared to GADA, respectively. Furthermore, 12.5%, 20.0%, and 20.0% of the 3 Screen ICA-positive patients were negative for GADA. Additionally, 1.3% of the AITD patients who tested negative for 3 Screen ICA in both the AITD with T2D and non-diabetic AITD groups were found to be positive for individual autoantibodies. Among the 3 Screen ICA-positive patients, there was a significantly higher proportion of individuals with multiple autoantibodies in AITD patients with T1D compared to those without diabetes (37.5% vs 5.0%, P < 0.05). However, this proportion was similar to that in AITD patients with T2D (20.0%). Nevertheless, there was no significant difference in 3 Screen ICA titers between AITD patients with T1D and those without diabetes (436.8 ± 66.4 vs 308.1 ± 66.4 index). Additionally, no significant difference in 3 Screen ICA titers was observed between Graves' disease and Hashimoto's thyroiditis in any of the groups. CONCLUSION: Our findings reveal that some AITD patients without diabetes exhibit 3 Screen ICA titers comparable to those in AITD patients with T1D. Thus, 3 Screen ICA outperforms GADA in identifying latent anti-islet autoantibody-positive individuals among AITD patients.

Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.

PURPOSE: Develop a diabetes diagnostic tool based on two markers of continuous glucose monitoring (CGM) dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). METHODS: 5,754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute two individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between eight glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden's index was used to determine "optimal" cut-points for ER and S for health vs. diabetes (case 1); type 1 vs. type 2 (case 2); and low vs. high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). RESULTS: Optimal cut-off points were determined for ER and S for each of the three cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI: 0.97-0.99) and AUC = 0.99 (95% CI: 0.99-1.00), respectively, (cut-offs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). CONCLUSIONS: CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing and identifying individuals at higher immunological risk of progressing to type 1 diabetes.

Advances in risk predictive performance of pre-symptomatic type 1 diabetes via the multiplex Antibody-Detection-by-Agglutination-PCR assay.

Introduction: Achieving early diagnosis of pre-symptomatic type 1 diabetes is critical to reduce potentially life-threatening diabetic ketoacidosis (DKA) at symptom onset, link patients to FDA approved therapeutics that can delay disease progression and support novel interventional drugs development. The presence of two or more islet autoantibodies in pre-symptomatic type 1 diabetes patients indicates high-risk of progression to clinical manifestation. Method: Herein, we characterized the capability of multiplex ADAP assay to predict type 1 diabetes progression. We obtained retrospective coded sera from a cohort of 48 progressors and 44 non-progressors from the NIDDK DPT-1 study. Result: The multiplex ADAP assay and radiobinding assays had positive predictive value (PPV)/negative predictive value (NPV) of 68%/92% and 67%/66% respectively. The improved NPV stemmed from 12 progressors tested positive for multiple islet autoantibodies by multiplex ADAP assay but not by RBA. Furthermore, 6 out of these 12 patients tested positive for multiple islet autoantibodies by RBA in subsequent sampling events with a median delay of 2.8 years compared to multiplex ADAP assay. Discussion: In summary, multiplex ADAP assay could be an ideal tool for type 1 diabetes risk testing due to its sample-sparing nature (4µL), non-radioactiveness, compatibility with widely available real-time qPCR instruments and favorable risk prediction capability.

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.

Progression of type 1 diabetes is associated with high levels of soluble PD-1 in islet autoantibody-positive children.

AIMS/HYPOTHESIS: Type 1 diabetes is an autoimmune disorder that is characterised by destruction of pancreatic beta cells by autoreactive T lymphocytes. Although islet autoantibodies (AAb) are an indicator of disease progression, specific immune biomarkers that can be used as target molecules to halt development of type 1 diabetes have not been discovered. Soluble immune checkpoint molecules (sICM) play a pivotal role in counteracting excessive lymphocyte responses, but their role in type 1 diabetes is unexplored. In this longitudinal study, we measured sICM levels in AAb-positive (AAb+) children to identify molecules related to type 1 diabetes progression. METHODS: We measured the levels of 14 sICM in the sera of AAb+ children (n=57) compared to those with recent-onset type 1 diabetes (n=79) and healthy children (n=44), obtained from two cohorts. AAb+ children were followed up and divided based on their progression to type 1 diabetes (AAbP) or not (AAbNP) (if they lost islet autoimmunity and did not develop disease in subsequent years). sICM were also measured in the sample taken at the visit closest to disease onset in AAbP children. RESULTS: We found that AAb+ children had a distinct sICM profile compared with healthy children and those with recent-onset type 1 diabetes. In addition, AAb+ children who progressed to type 1 diabetes (AAbP) had higher sICM concentrations than non-progressors (AAbNP). Further, sICM levels decreased in AAbP children close to disease onset. Application of Cox regression models highlighted that high concentrations of soluble programmed cell death protein 1 (sPD-1) are associated with type 1 diabetes progression (HR 1.71; 95% CI 1.16, 2.51; p=0.007). CONCLUSIONS/INTERPRETATION: This study reveals an sICM profile that is dysregulated during the preclinical stage of type 1 diabetes, and identifies sPD-1 as a pathophysiologically-relevant molecule that is associated with disease progression, offering a potential target for early interventions in autoimmune diabetes.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study.

BACKGROUND: Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data. METHODS: Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. RESULTS: The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88. CONCLUSIONS: A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.

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.

Children diagnosed with presymptomatic type 1 diabetes through public health screening have milder diabetes at clinical manifestation.

AIMS/HYPOTHESIS: We aimed to determine whether disease severity was reduced at onset of clinical (stage 3) type 1 diabetes in children previously diagnosed with presymptomatic type 1 diabetes in a population-based screening programme for islet autoantibodies. METHODS: Clinical data obtained at diagnosis of stage 3 type 1 diabetes were evaluated in 128 children previously diagnosed with presymptomatic early-stage type 1 diabetes between 2015 and 2022 in the Fr1da study and compared with data from 736 children diagnosed with incident type 1 diabetes between 2009 and 2018 at a similar age in the DiMelli study without prior screening. RESULTS: At the diagnosis of stage 3 type 1 diabetes, children with a prior early-stage diagnosis had lower median HbA1c (51 mmol/mol vs 91 mmol/mol [6.8% vs 10.5%], p<0.001), lower median fasting glucose (5.3 mmol/l vs 7.2 mmol/l, p<0.05) and higher median fasting C-peptide (0.21 nmol/l vs 0.10 nmol/l, p<0.001) compared with children without previous early-stage diagnosis. Fewer participants with prior early-stage diagnosis had ketonuria (22.2% vs 78.4%, p<0.001) or required insulin treatment (72.3% vs 98.1%, p<0.05) and only 2.5% presented with diabetic ketoacidosis at diagnosis of stage 3 type 1 diabetes. Outcomes in children with a prior early-stage diagnosis were not associated with a family history of type 1 diabetes or diagnosis during the COVID-19 pandemic. A milder clinical presentation was observed in children who participated in education and monitoring after early-stage diagnosis. CONCLUSIONS/INTERPRETATION: Diagnosis of presymptomatic type 1 diabetes in children followed by education and monitoring improved clinical presentation at the onset of stage 3 type 1 diabetes.

Presencia de autoanticuerpos anti islotes pancreáticos en diabetes tipo 1 durante la edad pediátrica / Presence of anti-pancreatic islet autoantibodies in type 1 diabetes in children

En la Diabetes tipo 1 (DM1) la pérdida de células ß pancreáticas es consecuencia de un proceso de autoinmunidad que cursa con la presencia de autoanticuerpos anti-islotes pancreáticos (AAPs). Estos AAPs son marcadores útiles para la clasificación de la enfermedad. En un centro pediátrico de tercer nivel se analizó la frecuencia de presentación de GADA, IA-2A, ZnT8A e IAA en un grupo con reciente debut entre enero 2018 y agosto 2021 (n= 90). Además, se investigó la frecuencia de presentación y relación de los AAPs con la edad, sexo y tiempo de evolución en pacientes en seguimiento (n= 240). En el grupo de debut se obtuvo positividad de GADA, IA-2A, ZnT8A y IAA en 77,8; 60; 62 y 47,8% de los pacientes respectivamente, un 4% no presentó AAPs. El 95,6% de los pacientes presentaron al menos un AAPs positivo. La frecuencia de IAA en el grupo en debut fue mayor en menores de 5 años. En el grupo en seguimiento el 75,2% resultaron GADA positivo (85,7% en mujeres y 62,8% en varones) p<0,05. IA-2A y ZnT8A fueron positivos en 45 y 51.7% respectivamente. El 91% presentaron al menos un AAP positivo. En este grupo se evidenció una menor positividad en función del tiempo de evolución. Se pudo determinar la frecuencia de presentación de los AAPs en un grupo en debut y la relación con la edad, sexo y tiempo de evolución en pacientes en seguimiento. La determinación de APPs facilita la correcta clasificación y elección de la terapia adecuada (AU)

In type 1 diabetes (DM1) the loss of pancreatic ß-cells is a consequence of an autoimmune process that results in the presence of pancreatic anti-islet autoantibodies (PAAs). PAAs are useful markers for the classification of the disease. The frequency of presentation of GADA, IA-2A, ZnT8A, and IAA in a group with recent debut seen between January 2018 and August 2021 (n= 90) was analyzed in a tertiary pediatric center. In addition, we investigated the frequency of presentation and association of PAAs with age, sex, and time of evolution in patients in follow-up (n= 240). In the debut group, GADA, IA2A, ZnT8A, and IAA positivity was found in 77.8, 60, 62, and 47.8% of patients, respectively; no PAAs were observed in 4% of the patients. Overall, 95.6% presented at least one positive PAA. The frequency of IAA in the debut group was higher in children younger than 5 years. In the follow-up group, 75.2% were GADA positive (85.7% of females and 62.8% of males) p<0.05. IA-2A and ZnT8A were positive in 45 and 51.7% respectively. Ninety-one percent presented with at least one positive PAA. In this group, a lower positivity was evidenced as a function of the time of evolution. The frequency of presentation of PAAs in a debut group and the relationship with age, sex, and time of evolution in patients in follow-up was demonstrated. The assessment of PAAs facilitates the correct classification and choice of adequate therapy (AU)

The countdown to type 1 diabetes: when, how and why does the clock start?

'The clock to type 1 diabetes has started when islet antibodies are first detected', commented George Eisenbarth with regard to the pathogenesis of type 1 diabetes. This review focuses on 'starting the clock', i.e. the initiation of pre-symptomatic islet autoimmunity/the first appearance of islet autoantibodies. In particular, this review addresses why susceptibility to developing islet autoimmunity is greatest in the first 2 years of life and why beta cells are a frequent target of the immune system during this fertile period. A concept for the development of beta cell autoimmunity in childhood is discussed and three factors are highlighted that contribute to this early predisposition: (1) high beta cell activity and potential vulnerability to stress; (2) high rates of and first exposures to infection; and (3) a heightened immune response, with a propensity for T helper type 1 (Th1) immunity. Arguments are presented that beta cell injury, accompanied by activation of an inflammatory immune response, precedes the initiation of autoimmunity. Finally, the implications for strategies aimed at primary prevention for a world without type 1 diabetes are discussed.

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2 with zero (n = 21), one (n = 18), and ≥2 (n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P = 0.04), overnight CGM incremental AUC (P = 0.005), and T180 for 75 min post-SLMM CGM traces (P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

The importance of biomarker development for monitoring type 1 diabetes progression rate and therapeutic responsiveness.

Type 1 diabetes (T1D) is an autoimmune condition of children and adults in which immune cells target insulin-producing pancreatic ß-cells for destruction. This results in a chronic inability to regulate blood glucose levels. The natural history of T1D is well-characterized in childhood. Evidence of two or more autoantibodies to the islet antigens insulin, GAD, IA-2 or ZnT8 in early childhood is associated with high risk of developing T1D in the future. Prediction of risk is less clear in adults and, overall, the factors controlling the progression rate from multiple islet autoantibody positivity to onset of symptoms are not fully understood. An anti-CD3 antibody, teplizumab, was recently shown to delay clinical progression to T1D in high-risk individuals including adults and older children. This represents an important proof of concept for those at risk of future T1D. Given their role in risk assessment, islet autoantibodies might appear to be the most obvious biomarkers to monitor efficacy. However, monitoring islet autoantibodies in clinical trials has shown only limited effects, although antibodies to the most recently identified autoantigen, tetraspanin-7, have not yet been studied in this context. Measurements of beta cell function remain fundamental to assessing efficacy and different models have been proposed, but improved biomarkers are required for both progression studies before onset of diabetes and in therapeutic monitoring. In this mini-review, we consider some established and emerging predictive and prognostic biomarkers, including markers of pancreatic function that could be integrated with metabolic markers to generate improved strategies to measure outcomes of therapeutic intervention.

SARS-CoV-2 Positive Serology and Islet Autoantibodies in Newly Diagnosed Pediatric Cases of Type 1 Diabetes Mellitus: A Single-Center Cohort Study.

Acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, although presenting less severe forms of the disease in children, seems to play a role in the development of other conditions, including type 1 diabetes mellitus (T1DM). After the beginning of the pandemic, an increase in the number of T1DM pediatric patients was observed in several countries, thus leading to many questions about the complex relationship between SARS-CoV-2 infection and T1DM. Our study aimed to highlight possible correlations between SARS-CoV-2 serology and T1DM onset. Therefore, we performed an observational retrospective cohort study that included 158 children diagnosed with T1DM in the period April 2021-April 2022. The presence or absence of SARS-CoV-2 and T1DM-specific antibodies and other laboratory findings were assessed. In the group of patients with positive SARS-CoV-2 serology, a higher percentage had detectable IA-2A antibodies, more children were positive for all three islet autoantibodies determined (GADA, ICA, and IA-2A), and a higher mean HbA1c value was found. No difference existed between the two groups regarding DKA presence and severity. A lower C-peptide level was found in the patients presenting diabetic ketoacidosis (DKA) at T1DM onset. When compared to a group of patients diagnosed before the pandemic, an increased incidence of both DKA and severe DKA, as well as a higher age at diagnosis and higher levels of HbA1c were present in our study group. These findings have important implications for the ongoing monitoring and management of children with T1DM after the COVID-19 pandemic and highlight the need for further research to better understand the complex relationship between SARS-CoV-2 infection and T1DM.

Comparison of positive rates between glutamic acid decarboxylase antibodies and ElisaRSR™ 3 Screen ICA™ in recently obtained sera from patients who had been previously diagnosed with slowly progressive type 1 diabetes.

AIMS/INTRODUCTION: This study aimed to compare the positivity rates of glutamic acid decarboxylase autoantibodies (GADA) and ElisaRSR™ 3 Screen ICA™ (3 Screen ICA), a newly developed assay for the simultaneous measurement of GADA, insulinoma-associated antigen-2 autoantibodies (IA-2A), and zinc transporter 8 autoantibodies (ZnT8A), in recently obtained sera from patients who had been previously diagnosed with slowly progressive type 1 diabetes (SPIDDM). MATERIALS AND METHODS: We enrolled 53 patients with SPIDDM who were positive for GADA at the diagnosis and 98 non-diabetic individuals, and investigated the diagnostic accuracy of the 3 Screen ICA (cutoff index ≥30 units) compared with that of GADA. In addition, we compared the clinical characteristics of patients with SPIDDM who were negative or positive on 3 Screen ICA. RESULTS: The positivity rates of 3 Screen ICA, GADA, IA-2A, and ZnT8A were 88.7, 86.8, 24.5, and 13.2%, respectively. The respective sensitivity, specificity, and positive and negative predictive values for SPIDDM were 88.7, 100, 100, and 94.2% by 3 Screen ICA and 86.8, 100, 100.0, and 93.3% by GADA. There were no significant differences in age at onset, duration of diabetes, body mass index, glycated hemoglobin and C-peptide levels, and the prevalence of autoimmune thyroiditis between patients with SPIDDM who were positive or negative on 3 Screen ICA. However, the prevalence of insulin users was significantly higher in those who were positive than in those who were negative on 3 Screen ICA. CONCLUSIONS: Similar to GADA, 3 Screen ICA may be a useful diagnostic tool for detecting patients with SPIDDM.

A Concerted Vision to Advance the Knowledge of Diabetes Mellitus Related to Immune Checkpoint Inhibitors.

The rubric of immune-related (ir) diabetes mellitus (DM) (irDM) encompasses various hyperglycemic disorders related to immune checkpoint inhibitors (ICPis). Beyond sharing similarities with conventional DM, irDM is a distinct, yet important, entity. The present narrative review provides a comprehensive overview of the literature regarding irDM published in major databases from January 2018 until January 2023. Initially considered rare, irDM is increasingly being reported. To advance the knowledge of irDM, the present review suggests a concerted vision comprising two intertwined aspects: a scientific-centered and a patient-centered view. The scientific-centered aspect addresses the pathophysiology of irDM, integrating: (i) ICPi-induced pancreatic islet autoimmunity in genetically predisposed patients; (ii) altered gut microbiome; (iii) involvement of exocrine pancreas; (iv) immune-related acquired generalized lipodystrophy. The patient-centered aspect is both nurtured by and nurturing the four pillars of the scientific-centered aspect: awareness, diagnosis, treatment, and monitoring of irDM. The path forward is a multidisciplinary initiative towards: (i) improved characterization of the epidemiological, clinical, and immunological profile of irDM; (ii) standardization of reporting, management, and surveillance protocols for irDM leveraging global registries; (iii) patient stratification according to personalized risk for irDM; (iv) new treatments for irDM; and (v) uncoupling ICPi efficacy from immunotoxicity.