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The glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease.

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

ObjectiveLimited information is available regarding youth-onset diabetes in Mali. We investigated demographic, clinical, biochemical, and genetic features in new diabetes cases in children and adolescents.Research design and methodsThe study was conducted at Hôpital du Mali in Bamako. A total of 132 recently-diagnosed cases

BackgroundBetter understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management.MethodsImmunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed.FindingsThe median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age ≥ 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63- 4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC.InterpretationIntegration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19.FundingNIH.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA‐approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model‐based clinical trial simulation tool. Using individual‐level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial‐specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web‐based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D. [ABSTRACT FROM AUTHOR]

Background: The Insomnia Severity Index (ISI) is a widely used measure of insomnia severity. Various ISI research findings suggest different factor solutions and meaningful within-individual change (MWIC) to detect treatment response in patients with insomnia. This study examined an ISI factor solution and psychometric indices to define MWIC in a robust patient sample from clinical trial settings. Methods: We endeavored to improve upon previous validation of ISI by examining structural components of confirmatory factor analysis (CFA) models using two large, placebo-controlled clinical trials of lemborexant for insomnia. Using the best-fitting two-factor solution, we evaluated anchor-based, distribution-based and receiver operating characteristic (ROC) curve methods to derive an estimate of the MWIC. Results: The model structure for the 7-item scale proposed in other research did not fit the observed data from our two lemborexant clinical trials (N = 1956) as well as a two-factor solution based on 6 items did. Using triangulation of anchor-based, distribution-based, and ROC methods, we determined that a 5-point reduction using 6 items best represented a clinically meaningful improvement in individuals with insomnia in our patient sample. Conclusions: A 6-item two-factor scale had better psychometric properties than the 7-item scale in this patient sample. On the 6-item scale, a reduction of 5 points in the ISI total score represented the MWIC. Generalizability of the proposed MWIC may be limited to patient populations with similar demographic and clinical characteristics. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment associated with schizophrenia (CIAS) represents a distinct, persistent, and core group of schizophrenia symptoms. Cognitive symptoms have been shown to have an impact on quality of life. There are several published CIAS measures, but none based on direct patient self-report. It is important to capture the patient's perspective to supplement performancebased outcome measures of cognition to provide a complete picture of the patient's experience. This paper describes additional validation work on the Patient-Reported Experience of Cognitive Impairment in Schizophrenia (PRECIS) instrument. Methods: Data from two large, international, pharmaceutical clinical trials in medically and psychiatrically stable English-speaking patients with schizophrenia and 88 healthy controls were analyzed. An exploratory factor analysis (EFA) was conducted in one trial (n = 215), using the original 35-item PRECIS. The factor structure suggested by EFA was further evaluated using item response theory (IRT; Samejima's graded response model), and tested using confirmatory factor analysis (CFA). Both EFA and CFA results were tested in a second trial with similar inclusion/exclusion characteristics (n = 410). Additional statistical properties were evaluated in healthy controls. Results: EFA suggested that the best solution after item reduction suggested a factor structure of 6 factors based on 26 items (memory, communication, self-control, executive function, attention, sharpness of thought), supporting a total score, with an additional 2-item bother score (28 items in all). IRT analysis indicated the items were well-ordered within each domain. The CFA demonstrated excellent model fit, accounting for 69% of the variance. The statistical properties of the 28-item version of the PRECIS were confirmed in the second trial. Evidence for internal consistency and test-retest reliability was robust. Known-groups validity was supported by comparison of healthy controls with patients with schizophrenia. Correlations indicated moderate associations between PRECIS and functioning instruments like the Schizophrenia Cognition Rating Scale (SCoRS), but weak correlations with performance-based outcomes like MATRICS Consensus Cognitive Battery (MCCB). Discussion: Using two clinical trial samples, we identified a robust factor structure for the PRECIS and were able to replicate it in the second sample. Evaluation of the meaningful score difference (MSD) should be repeated in future studies, as these samples did not show enough change for it to be evaluated. Conclusions: This analysis provides strong evidence for the reliability and validity of the PRECIS, a 28-item, patient-reported instrument to assess cognitive impairment associated with schizophrenia. The correlation with functioning and the weak correlation with performance on cognitive tasks suggests that patient reports of cognitive impairment measure a unique aspect of patient experience. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Mixed-meal tolerance test–stimulated area under the curve (AUC) C-peptide at 12–24 months represents the primary end point for nearly all intervention trials seeking to preserve β-cell function in recent-onset type 1 diabetes. We hypothesized that participant benefit might be detected earlier and predict outcomes at 12 months posttherapy. Such findings would support shorter trials to establish initial efficacy. RESEARCH DESIGN AND METHODS: We examined data from six Type 1 Diabetes TrialNet immunotherapy randomized controlled trials in a post hoc analysis and included additional stimulated metabolic indices beyond C-peptide AUC. We partitioned the analysis into successful and unsuccessful trials and analyzed the data both in the aggregate as well as individually for each trial. RESULTS: Among trials meeting their primary end point, we identified a treatment effect at 3 and 6 months when using C-peptide AUC (P = 0.030 and P < 0.001, respectively) as a dynamic measure (i.e., change from baseline). Importantly, no such difference was seen in the unsuccessful trials. The use of C-peptide AUC as a 6-month dynamic measure not only detected treatment efficacy but also suggested long-term C-peptide preservation (R 2 for 12-month C-peptide AUC adjusted for age and baseline value was 0.80, P < 0.001), and this finding supported the concept of smaller trial sizes down to 54 participants. CONCLUSIONS: Early dynamic measures can identify a treatment effect among successful immune therapies in type 1 diabetes trials with good long-term prediction and practical sample size over a 6-month period. While external validation of these findings is required, strong rationale and data exist in support of shortening early-phase clinical trials. [ABSTRACT FROM AUTHOR]

BACKGROUND. Multiple islet autoantibodies (AAbs) predict the development of type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in only approximately 15% of individuals who are positive for single AAbs (generally against glutamic acid decarboxylase [GADA]); hence, the single [GADA.sup.+] state may represent an early stage of T1D. METHODS. Here, we functionally, histologically, and molecularly phenotyped human islets from nondiabetic [GADA.sup.+] and T1D donors. RESULTS. Similar to the few remaining [beta] cells in the T1D islets, [GADA.sup.+] donor islets demonstrated a preserved insulin secretory response. By contrast, [alpha] cell glucagon secretion was dysregulated in both [GADA.sup.+] and T1D islets, with impaired glucose suppression of glucagon secretion. Single-cell RNA-Seq of [GADA.sup.+] [alpha] cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of cAMP-dependent protein kinase inhibitor p (PKIB), providing a molecular basis for the loss of glucose suppression and the increased effect of 3-isobutyl-1-methylxanthine (IBMX) observed in [GADA.sup.+] donor islets. CONCLUSION. We found that [alpha] cell dysfunction was present during the early stages of islet autoimmunity at a time when p cell mass was still normal, raising important questions about the role of early [alpha] cell dysfunction in the progression of T1D. FUNDING. This work was supported by grants from the NIH (3UC4DK112217-01S1, U01DK123594-02, UC4DK112217, UC4DK112232, U01DK123716, and P30 DK019525) and the Vanderbilt Diabetes Research and Training Center (DK20593)., Introduction Longitudinal studies have shown that individuals who are at high genetic risk for or have a family history of type 1 diabetes (T1D), who later develop diabetes, progress through [...]

Introduction: The antigen-presenting cell function of insulin-reactive B cells promotes type 1 diabetes (T1D) in non-obese diabetic (NOD) mice by stimulating pathogenic T cells leading to destruction of insulin-producing b-cells of pancreatic islets. Methods/Results: To target insulin-reactive B cells, AKS-107, a human IgG1 Fc molecule fused with human insulin A and B chains, was engineered to retain conformational insulin epitopes that bound mouse and human B cell receptors but prevented binding to the insulin metabolic receptor. AKS-107 Fc-mediated deletion of insulin-reactive B cells was demonstrated via ex vivo and in vivo experiments with insulin-reactive B cell receptor transgenic mouse strains, VH125Tg/NOD and Tg125(H+L)/NOD. As an additional immune tolerance feature, the Y16A mutation of the insulin B(9-23) dominant T cell epitope was engineered into AKS-107 to suppress activation of insulin-specific T cells. In mice and non-human primates, AKS-107 was well-tolerated, non-immunogenic, did not cause hypoglycemia even at high doses, and showed an expectedly protracted pharmacokinetic profile. AKS-107 reproducibly prevented spontaneous diabetes from developing in NOD and VH125Tg/NOD mice that persisted for months after cessation of treatment, demonstrating durable immune tolerance. Discussion: These preclinical outcomes position AKS-107 for clinical development in T1D prevention settings. [ABSTRACT FROM AUTHOR]

Aims: Determining diabetes type in children has become increasingly difficult due to an overlap in typical characteristics between type 1 diabetes (T1D) and type 2 diabetes (T2D). The Diabetes Study in Children of Diverse Ethnicity and Race (DISCOVER) programme is a National Institutes of Health (NIH)‐supported multicenter, prospective, observational study that enrols children and adolescents with non‐secondary diabetes. The primary aim of the study was to develop improved models to differentiate between T1D and T2D in diverse youth. Materials and Methods: The proposed models will evaluate the utility of three existing T1D genetic risk scores in combination with data on islet autoantibodies and other parameters typically available at the time of diabetes onset. Low non‐fasting serum C‐peptide (<0.6 nmol/L) between 3 and 10 years after diabetes diagnosis will be considered a biomarker for T1D as it reflects the loss of insulin secretion ability. Participating centres are enrolling youth (<19 years old) either with established diabetes (duration 3–10 years) for a cross‐sectional evaluation or with recent onset diabetes (duration 3 weeks–15 months) for the longitudinal observation with annual visits for 3 years. Cross‐sectional data will be used to develop models. Longitudinal data will be used to externally validate the best‐fitting model. Results: The results are expected to improve the ability to classify diabetes type in a large and growing subset of children who have an unclear form of diabetes at diagnosis. Conclusions: Accurate and timely classification of diabetes type will help establish the correct clinical management early in the course of the disease. [ABSTRACT FROM AUTHOR]

Public adoption of preventative behaviors to reduce the transmission of COVID‐19 is crucial to managing the pandemic, and so it is vital to determine what factors influence the uptake of those behaviors. Previous studies have identified COVID‐19 risk perceptions as a key factor, but this work has typically been limited both in assuming that risk means risk to the personal self, and in being reliant on self‐reported data. Drawing on the social identity approach, we conducted two online studies in which we investigated the effects of two different types of risk on preventative measure taking: risk to the personal self and risk to the collective self (i.e., members of a group with which one identifies). Both studies involved behavioral measures using innovative interactive tasks. In Study 1 (n = 199; data collected 27 May 2021), we investigated the effects of (inter)personal and collective risk on physical distancing. In Study 2 (n = 553; data collected 20 September 2021), we investigated the effects of (inter)personal and collective risk on the speed at which tests are booked as COVID‐19 symptoms develop. In both studies, we find that perceptions of collective risk, but not perceptions of (inter)personal risk, influence the extent to which preventative measures are adopted. We discuss the implications both conceptually (as they relate to both the conceptualization of risk and social identity processes) and also practically (in terms of the implications for public health communications). [ABSTRACT FROM AUTHOR]

OBJECTIVE: Low-dose antithymocyte globulin (ATG) (2.5 mg/kg) preserves C-peptide and reduces HbA1c in new-onset stage 3 type 1 diabetes, yet efficacy in delaying progression from stage 2 to stage 3 has not been evaluated. RESEARCH DESIGN AND METHODS: Children (n = 6) aged 5–14 years with stage 2 type 1 diabetes received off-label, low-dose ATG. HbA1c, C-peptide, continuous glucose monitoring, insulin requirements, and side effects were followed for 18–48 months. RESULTS: Three subjects (50%) remained diabetes free after 1.5, 3, and 4 years of follow-up, while three developed stage 3 within 1–2 months after therapy. Eighteen months posttreatment, even disease progressors demonstrated near-normal HbA1c (5.1% [32 mmol/mol], 5.6% [38 mmol/mol], and 5.3% [34 mmol/mol]), time in range (93%, 88%, and 98%), low insulin requirements (0.17, 0.18, and 0.34 units/kg/day), and robust C-peptide 90 min after mixed meal (1.3 ng/dL, 2.3 ng/dL, and 1.4 ng/dL). CONCLUSIONS: These observations support additional prospective studies evaluating ATG in stage 2 type 1 diabetes. [ABSTRACT FROM AUTHOR]

Objective Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence. Materials and methods We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study. Results Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019. Conclusion Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance. [ABSTRACT FROM AUTHOR]

Over the last two decades, increased availability of human pancreatic tissues has allowed for major expansions in our understanding of islet biology in health and disease. Indeed, studies of fixed and frozen pancreatic tissues, as well as efforts using viable isolated islets obtained from organ donors, have provided significant insights toward our understanding of diabetes. However, the procedures associated with islet isolation result in distressed cells that have been removed from any surrounding influence. The pancreas tissue slice technology was developed as an in situ approach to overcome certain limitations associated with studies on isolated islets or fixed tissue. In this Perspective, we discuss the value of this novel platform and review how pancreas tissue slices, within a short time, have been integrated in numerous studies of rodent and human islet research. We show that pancreas tissue slices allow for investigations in a less perturbed organ tissue environment, ranging from cellular processes, over peri-islet modulations, to tissue interactions. Finally, we discuss the considerations and limitations of this technology in its future applications. We believe the pancreas tissue slices will help bridge the gap between studies on isolated islets and cells to the systemic conditions by providing new insight into physiological and pathophysiological processes at the organ level. Article Highlights: Human pancreas tissue slices represent a novel platform to study human islet biology in close to physiological conditions. Complementary to established technologies, such as isolated islets, single cells, and histological sections, pancreas tissue slices help bridge our understanding of islet physiology and pathophysiology from single cell to intact organ. Diverse sources of viable human pancreas tissue, each with distinct characteristics to be considered, are available to use in tissue slices for the study of diabetes pathogenesis. [ABSTRACT FROM AUTHOR]