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 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, HbA1c 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 T1D. 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; lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of rate of disease progression within Stage 2. CONCLUSIONS: 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.

A phase 2 randomized trial with autologous polyclonal expanded regulatory T cells in children with new-onset type 1 diabetes.

CD4+CD25hiCD127lo/-FOXP3+ regulatory T cells (Tregs) play a key role in preventing autoimmunity. In autoimmune type 1 diabetes (T1D), adoptive transfer of autologous polyclonal Tregs has been shown to be safe in adults in phase 1 clinical trials. We explored factors contributing to efficacy of autologous polyclonal expanded Tregs (expTregs) in a randomized phase 2 multi-center, double-blind, clinical trial (Sanford/Lisata Therapeutics T-Rex phase 2 trial, ClinicalTrials.gov NCT02691247). One hundred ten treated children and adolescents with new-onset T1D were randomized 1:1:1 to high-dose (20 × 106 cells/kilogram) or low-dose (1 × 106 cells/kilogram) treatments or to matching placebo. Cytometry as well as bulk and single-cell RNA sequencing were performed on selected expTregs and peripheral blood samples from participants. The single doses of expTregs were safe but did not prevent decline in residual ß cell function over 1 year compared to placebo (P = 0.94 low dose, P = 0.21 high dose), regardless of age or baseline C-peptide. ExpTregs were highly activated and suppressive in vitro. A transient increase of activated memory Tregs was detectable 1 week after infusion in the high-dose cohort, suggesting effective transfer of expTregs. However, the in vitro fold expansion of expTregs varied across participants, even when accounting for age, and lower fold expansion and its associated gene signature were linked with better C-peptide preservation regardless of Treg dose. These results suggest that a single dose of polyclonal expTregs does not alter progression in T1D; instead, Treg quality may be an important factor.

Abatacept increases T cell exhaustion in early RA individuals who carry HLA risk alleles.

Exhausted CD8 T cells (TEX) are associated with worse outcome in cancer yet better outcome in autoimmunity. Building on our past findings of increased TIGIT+KLRG1+ TEX with teplizumab therapy in type 1 diabetes (T1D), in the absence of treatment we found that the frequency of TIGIT+KLRG1+ TEX is stable within an individual but differs across individuals in both T1D and healthy control (HC) cohorts. This TIGIT+KLRG1+ CD8 TEX population shares an exhaustion-associated EOMES gene signature in HC, T1D, rheumatoid arthritis (RA), and cancer subjects, expresses multiple inhibitory receptors, and is hyporesponsive in vitro, together suggesting co-expression of TIGIT and KLRG1 may broadly define human peripheral exhausted cells. In HC and RA subjects, lower levels of EOMES transcriptional modules and frequency of TIGIT+KLRG1+ TEX were associated with RA HLA risk alleles (DR0401, 0404, 0405, 0408, 1001) even when considering disease status and cytomegalovirus (CMV) seropositivity. Moreover, the frequency of TIGIT+KLRG1+ TEX was significantly increased in RA HLA risk but not non-risk subjects treated with abatacept (CTLA4Ig). The DR4 association and selective modulation with abatacept suggests that therapeutic modulation of TEX may be more effective in DR4 subjects and TEX may be indirectly influenced by cellular interactions that are blocked by abatacept.

A standardized metric to enhance clinical trial design and outcome interpretation in type 1 diabetes.

The use of a standardized outcome metric enhances clinical trial interpretation and cross-trial comparison. If a disease course is predictable, comparing modeled predictions with outcome data affords the precision and confidence needed to accelerate precision medicine. We demonstrate this approach in type 1 diabetes (T1D) trials aiming to preserve endogenous insulin secretion measured by C-peptide. C-peptide is predictable given an individual's age and baseline value; quantitative response (QR) adjusts for these variables and represents the difference between the observed and predicted outcome. Validated across 13 trials, the QR metric reduces each trial's variance and increases statistical power. As smaller studies are especially subject to random sampling variability, using QR as the outcome introduces alternative interpretations of previous clinical trial results. QR can provide model-based estimates that quantify whether individuals or groups did better or worse than expected. QR also provides a purer metric to associate with biomarker measurements. Using data from more than 1300 participants, we demonstrate the value of QR in advancing disease-modifying therapy in T1D. QR applies to any disease where outcome is predictable by pre-specified baseline covariates, rendering it useful for defining responders to therapy, comparing therapeutic efficacy, and understanding causal pathways in disease.

OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals.

CONTEXT: The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown. OBJECTIVE: Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D. METHODS: At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed. RESULTS: Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value). CONCLUSION: Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials.

Early introduction of peanut reduces peanut allergy across risk groups in pooled and causal inference analyses.

BACKGROUND: The Learning Early About Peanut allergy (LEAP) study has shown the effectiveness of early peanut introduction in prevention of peanut allergy (PA). In the Enquiring About Tolerance (EAT) study, a statistically significant reduction in PA was present only in per-protocol (PP) analyses, which can be subject to bias. OBJECTIVE: The aim of this study was to combine individual-level data from the LEAP and EAT trials and provide robust evidence on the bias-corrected, causal effect of early peanut introduction. METHOD: As part of the European Union-funded iFAAM project, this pooled analysis of individual pediatric patient data combines and compares effectiveness and efficacy estimates of oral tolerance induction among different risk strata and analysis methods. RESULTS: An intention-to-treat (ITT) analysis of pooled data showed a 75% reduction in PA (p < .0001) among children randomized to consume peanut from early infancy. A protective effect was present across all eczema severity groups, irrespective of enrollment sensitization to peanut, and across different ethnicities. Earlier age of introduction was associated with improved effectiveness of the intervention. In the pooled PP analysis, peanut consumption reduced the risk of PA by 98% (p < .0001). A causal inference analysis confirmed the strong PP effect (89% average treatment effect relative risk reduction p < .0001). A multivariable causal inference analysis approach estimated a large (100%) reduction in PA in children without eczema (p = .004). CONCLUSION: We demonstrate a significant reduction in PA with early peanut introduction in a large group of pooled, randomized participants. This significant reduction was demonstrated across all risk subgroups, including children with no eczema. Furthermore, our results point to increased efficacy of the intervention with earlier age of introduction.

Risk Modeling to Reduce Monitoring of an Autoantibody-Positive Population to Prevent DKA at Type 1 Diabetes Diagnosis.

CONTEXT: The presence of islet autoimmunity identifies individuals likely to progress to clinical type 1 diabetes (T1D). In clinical research studies, autoantibody screening followed by regular metabolic monitoring every 6 months reduces incidence of diabetic ketoacidosis (DKA) at diagnosis. OBJECTIVE: We hypothesized that DKA reduction can be achieved on a population basis with a reduced frequency of metabolic monitoring visits. We reasoned that prolonged time between the development of T1D and the time of clinical diagnosis ("undiagnosed time") would more commonly result in DKA and thus that limiting undiagnosed time would decrease DKA. METHODS: An analysis was conducted of data from TrialNet's Pathway to Prevention (PTP), a cross-sectional longitudinal study that identifies and follows at-risk relatives of people with T1D. PTP is a population-based study enrolling across multiple countries. A total of 6193 autoantibody (AAB)-positive individuals participated in PTP from March 2004 to April 2019. We developed models of progression to clinical diagnosis for pediatric and adult populations with single or multiple AAB, and summarized results using estimated hazard rate. An optimal monitoring visit schedule was determined for each model to achieve a minimum average level of undiagnosed time for each population. RESULTS: Halving the number of monitoring visits usually conducted in research studies is likely to substantially lower the population incidence of DKA at diagnosis of T1D. CONCLUSION: Our study has clinical implications for the metabolic monitoring of at-risk individuals. Fewer monitoring visits would reduce the clinical burden, suggesting a path toward transitioning monitoring beyond the research setting.

Characterising the age-dependent effects of risk factors on type 1 diabetes progression.

AIMS/HYPOTHESIS: Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age. METHODS: Using longitudinal data from single- and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes. RESULTS: Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibody-positive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single- and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single- and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate. CONCLUSIONS/INTERPRETATION: Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.

Early Prognostic Indicators of Subsequent Hospitalization in Patients with Mild COVID-19.

Comprehensive data on early prognostic indicators in patients with mild COVID-19 remains sparse. In this single center case series, we characterized the initial clinical presentation in 180 patients with mild COVID-19 and defined the earliest predictors of subsequent deterioration and need for hospitalization. Three broad patient phenotypes and four symptom clusters were characterized, differentiated by varying risk for adverse outcomes. Among 14 symptoms assessed, subjective shortness of breath (SOB) most strongly associated with adverse outcomes (odds ratio (OR) 21.3, 95% confidence interval (CI): 2.7-166.4; p < 0.0001). In combination, SOB and number of comorbidities were highly predictive of subsequent hospitalization (area under the curve (AUC) 92%). Additionally, initial lymphopenia (OR 21.0, 95% CI: 2.1-210.1; p = 0.002) and male sex (OR 3.5, 95% CI: 0.9-13.0; p = 0.05) were associated with increased risk of poor outcomes. Patients with known comorbidities, especially multiple, and those presenting with subjective SOB or lymphopenia should receive close monitoring and consideration for preemptive treatment, even when presenting with mild symptoms.

Circulating Unmethylated Insulin DNA As a Biomarker of Human Beta Cell Death: A Multi-laboratory Assay Comparison.

CONTEXT: There is an unmet need for biomarkers of pancreatic beta-cell death to improve early diagnosis of type 1 diabetes, enroll subjects into clinical trials, and assess treatment response. To address this need, several groups developed assays measuring insulin deoxyribonucleic acid (DNA) with unmethylated CpG sites in cell-free DNA. Unmethylated insulin DNA should be derived predominantly from beta-cells and indicate ongoing beta-cell death. OBJECTIVE: To assess the performance of three unmethylated insulin DNA assays. DESIGN AND PARTICIPANTS: Plasma or serum samples from 13 subjects undergoing total pancreatectomy and islet autotransplantation were coded and provided to investigators to measure unmethylated insulin DNA. Samples included a negative control taken post-pancreatectomy but pretransplant, and a positive control taken immediately following islet infusion. We assessed technical reproducibility, linearity, and persistence of detection of unmethylated insulin DNA for each assay. RESULTS: All assays discriminated between the negative sample and samples taken directly from the islet transplant bag; 2 of 3 discriminated negative samples from those taken immediately after islet infusion. When high levels of unmethylated insulin DNA were present, technical reproducibility was generally good for all assays. CONCLUSIONS: The measurement of beta cell cell-free DNA, including insulin, is a promising approach, warranting further testing and development in those with or at-risk for type 1 diabetes, as well as in other settings where understanding the frequency or kinetics of beta cell death could be useful.

High residual C-peptide likely contributes to glycemic control in type 1 diabetes.

BACKGROUNDResidual C-peptide is detected in many people for years following the diagnosis of type 1 diabetes; however, the physiologic significance of low levels of detectable C-peptide is not known.METHODSWe studied 63 adults with type 1 diabetes classified by peak mixed-meal tolerance test (MMTT) C-peptide as negative (<0.007 pmol/mL; n = 15), low (0.017-0.200; n = 16), intermediate (>0.200-0.400; n = 15), or high (>0.400; n = 17). We compared the groups' glycemia from continuous glucose monitoring (CGM), ß cell secretory responses from a glucose-potentiated arginine (GPA) test, insulin sensitivity from a hyperinsulinemic-euglycemic (EU) clamp, and glucose counterregulatory responses from a subsequent hypoglycemic (HYPO) clamp.RESULTSLow and intermediate MMTT C-peptide groups did not exhibit ß cell secretory responses to hyperglycemia, whereas the high C-peptide group showed increases in both C-peptide and proinsulin (P ≤ 0.01). All groups with detectable MMTT C-peptide demonstrated acute C-peptide and proinsulin responses to arginine that were positively correlated with peak MMTT C-peptide (P < 0.0001 for both analytes). During the EU-HYPO clamp, C-peptide levels were proportionately suppressed in the low, intermediate, and high C-peptide compared with the negative group (P ≤ 0.0001), whereas glucagon increased from EU to HYPO only in the high C-peptide group compared with negative (P = 0.01). CGM demonstrated lower mean glucose and more time in range for the high C-peptide group.CONCLUSIONThese results indicate that in adults with type 1 diabetes, ß cell responsiveness to hyperglycemia and α cell responsiveness to hypoglycemia are observed only at high levels of residual C-peptide that likely contribute to glycemic control.FUNDINGFunding for this work was provided by the Leona M. and Harry B. Helmsley Charitable Trust, the National Center for Advancing Translational Sciences, and the National Institute of Diabetes and Digestive and Kidney Diseases.

Perioperative anaphylaxis in children: Etiology, time sequence, and patterns of clinical reactivity.

BACKGROUND: Perioperative anaphylaxis (PA) in children is an uncommon but potentially life-threatening complication associated with anesthesia. Early identification and management of PA is essential to optimize clinical outcomes. METHODS: We performed a retrospective study of anesthesia records from pediatric patients with PA from centers in the United Kingdom, France, and the United States over a period of 10 years. Time sequence of clinical signs and physiological variables during PA were collected, along with results of allergy testing. RESULTS: Twenty-nine children with PA were included. Median age was 11 years. Based on the modified Ring and Messmer Grading Scale, severe reactions were seen in 25 (86%) members of this cohort, with 4 (14%) experiencing cardiac arrest. Life-threatening hypotension was the first clinical sign of PA in 59% of cases, followed by tachycardia and bronchospasm. In 16 (55%) cases, the initial signs of PA involved multiple organ systems. When the initial signs of PA were cardiovascular and/or respiratory, more epinephrine doses were administered. Average time from initial sign of PA to treatment with epinephrine was 6 minutes (SD: 6, range: 1-25). The causative allergen was identified in 15 patients. CONCLUSION: Severe hypotension is the most common presenting sign of PA in children. Initial cardiovascular and/or respiratory signs are associated with the need for increased epinephrine doses. Further studies should optimize the prediction, identification, and early management of PA in children.