Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories

Investigating the impact of steroid dependence on gastrointestinal surgical outcomes from UK Biobank

Although corticosteroids are an important treatment for inflammatory bowel disease (IBD) patients, many subjects develop dependence, leading to serious long-term side effects. We applied causal inference analyses to investigate the length of steroid use on reoperations in IBD patients. We identified subjects in the UK Biobank general practice dataset with at least one major GI surgery and followed them for at least 5 years to evaluate subsequent operations. We defined steroid dependence as at least 12 weeks of use (vs. acute steroid use) prior to baseline surgery. Of the 363 subjects included in our analyses, 163 (45%) were prescribed steroids on or before baseline surgery, and of these (N = 125 of 163, 77%) were dependent. Additional analyses for time-dependent data on prescriptions found a link between prescription length and reoperation. Among UC subjects with acute use, the odds of reoperation were significantly lower (OR: 0.32, 95% CI: 0.0-0.73). Steroid dependence resulted in a delay of reoperation (median 1.2 vs. 2.3 years, P = 0.01). Our findings indicate that long-term steroid use tends to increase the need for reoperation, whereas short-term use may reduce it.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

The immunology of type 1 diabetes

Following the seminal discovery of insulin a century ago, treatment of individuals with type 1 diabetes (T1D) has been largely restricted to efforts to monitor and treat metabolic glucose dysregulation. The recent regulatory approval of the first immunotherapy that targets T cells as a means to delay the autoimmune destruction of pancreatic β-cells highlights the critical role of the immune system in disease pathogenesis and tends to pave the way for other immune-targeted interventions for T1D. Improving the efficacy of such interventions across the natural history of the disease will probably require a more detailed understanding of the immunobiology of T1D, as well as technologies to monitor residual β-cell mass and function. Here we provide an overview of the immune mechanisms that underpin the pathogenesis of T1D, with a particular emphasis on T cells.

Viruses as a potential environmental trigger of type 1 diabetes mellitus (Review)

The etiopathogenesis of type 1 diabetes mellitus (T1DM) is a complex multifactorial process that involves an intricate network of genetic, epigenetic, immunological, and environmental factors. Despite the advances in recent years, some aspects of the mechanisms involved in triggering the disease are still unclear. Infections with certain viruses have been suggested as possible environmental triggers for the autoimmune process that leads to selective and progressive destruction of pancreatic β-cells and insufficiency of insulin production, which is its hallmark. In this review, advances in knowledge and evidence that suggest the participation of certain viruses in the mechanisms of disease initiation and progression are described. It has been accepted that environmental factors, including viruses, can initiate and possibly sustain, accelerate, or slow down the autoimmune process and consequently damage insulin-producing pancreatic β-cells. Although the role of these agents, especially human enteroviruses, has been exhaustively studied as the most likely triggers of the activation of autoimmunity that destroys pancreatic islets and leads to T1DM, certain doubts remain. Clinical epidemiological and experimental studies in humans and animals provide consistent and increasing evidence that persistent viral infections, especially with human enteroviruses and rotavirus infections, are associated with an increased risk of the disease in individuals genetically predisposed to autoimmunity.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

BACKGROUND

Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.

METHODS

We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.

RESULTS

Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.

CONCLUSIONS

Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Feasibility and Validity of In-Home Self-Collected Capillary Blood Spot Screening for Type 1 Diabetes Risk

Aims: Self-collection of a blood sample for autoantibody testing has potential to facilitate screening for type 1 diabetes risk. We sought to determine the feasibility and acceptability of this approach and the performance of downstream antibody assays. Methods: People living with type 1 diabetes and their family members (N = 97) provided paired capillary blood spot and serum samples collected, respectively, by themselves and a health worker. They provided feedback on the ease, convenience, and painfulness of blood spot collection. Islet antibodies were measured in blood spots by antibody detection by agglutination PCR (ADAP) or multiplex enzyme-linked immunoassay (ELISA), and in serum by radioimmunoassay (RIA) or ELISA. Results: Using serum RIA and ELISA to define antibody status, 50 antibody-negative (Abneg) and 47 antibody-positive (Abpos) participants enrolled, of whom 43 and 47, respectively, returned testable blood spot samples. The majority indicated that self-collection was easier, more convenient, and less painful than formal venesection. The sensitivity and specificity for detection of Abpos by blood spot were, respectively, 85% and 98% for ADAP and 87% and 100% for multiplex ELISA. The specificities by ADAP for each of the four antigen specificities ranged from 98% to 100% and areas under the receiver operator curve from 0.841 to 0.986. Conclusions: Self-collected blood spot sampling is preferred over venesection by research participants. ADAP and multiplex ELISA are highly specific assays for islet antibodies in blood spots with acceptable performance for use alone or in combination to facilitate screening for type 1 diabetes risk. Clinical Trial Registration number: ACTRN12620000510943.

Screening and Prevention of Type 1 Diabetes: Where Are We?

A diagnosis of type 1 diabetes (T1D) and the subsequent requirement for exogenous insulin treatment is associated with considerable acute and chronic morbidity and a substantial effect on patient quality of life. Importantly, a large body of work suggests that early identification of presymptomatic T1D can accurately predict clinical disease, and when paired with education and monitoring, can yield improved health outcomes. Furthermore, a growing cadre of effective disease-modifying therapies provides the potential to alter the natural history of early stages of T1D. In this mini review, we highlight prior work that has led to the current landscape of T1D screening and prevention, as well as challenges and next steps moving into the future of these rapidly evolving areas of patient care.

Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity

OBJECTIVE

To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA).

RESEARCH DESIGN AND METHODS

Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis.

RESULTS

Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85-92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5-40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/Persistent/2 status and mIA to stage 3 type 1 diabetes.

CONCLUSIONS

The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA.

Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes

Better understanding of the early events in the development of type 1 diabetes is needed to improve prediction and monitoring of the disease progression during the substantially heterogeneous presymptomatic period of the beta cell damaging process. To address this concern, we used mass spectrometry-based proteomics to analyse longitudinal pre-onset plasma sample series from children positive for multiple islet autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n = 10) and compared these with similar measurements from matched children who were either positive for a single autoantibody (n = 10) or autoantibody negative (n = 10). Following statistical analysis of the longitudinal data, targeted serum proteomics was used to verify 11 proteins putatively associated with the disease development in a similar yet independent and larger cohort of children who progressed to the disease within 5 years of age (n = 31) and matched autoantibody negative children (n = 31). These data reiterated extensive age-related trends for protein levels in young children. Further, these analyses demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who progressed to type 1 diabetes, in comparison to autoantibody negative children.

Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study

BACKGROUND

Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10-18 years with an increased risk of developing type 1 diabetes.

METHODS

Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes.

FINDINGS

Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5-20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34-0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50-0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1-1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6-9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86-95) sensitive, with a positive predictive value of 66% (60-72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89-97) but lowered the positive predictive value to 55% (49-60).

INTERPRETATION

Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials.

FUNDING

JDRF International.

A Pilot Study on the Co-existence of Diabetes and Endometriosis in Reproductive-Age Women: Potential for Endometriosis Progression

Endometriosis (ENDO) is a chronic estrogen-dependent gynecological condition that affects reproductive-age women, causing pelvic pain, infertility, and increased risk for ovarian cancer. Diabetes mellitus (DM) is a metabolic disease with significant morbidity and mortality and rising incidence worldwide. The occurrence of DM among ENDO patients remains understudied, despite commonalities in these conditions' immune, inflammatory, and metabolic dysfunctions. This pilot study evaluated whether a subset of women with ENDO manifests DM co-morbidity and if so, whether DM promotes ENDO status. Archived ectopic lesions obtained at ENDO surgery from non-diabetic (ENDO-N; n = 11) and diabetic (ENDO-DM; n = 15) patients were identified by a search of an electronic health database. Retrieved samples were analyzed by immunohistochemistry for markers of proliferation (Ki67, PTEN), steroid receptor signaling (ESR, PGR) and macrophage infiltration (CD68). Immunostaining data were expressed as percentages of immune-positive cells in lesion stroma and epithelium. In lesion stroma, the percentages of nuclear immune-positive cells were higher for ESR2 and lower for PGR-T, in ENDO-DM than ENDO-N patients. The percentages of nuclear immune-positive cells for ESR1 and PTEN tended to be higher and lower, respectively, in ENDO-DM than ENDO-N groups. In lesion glandular epithelium, the percentages of nuclear immune-positive cells were higher for ESR1 and ESR2, in ENDO-DM than ENDO-N groups. ENDO-N lesions had lower percentages of stromal CD68 immune-positive cells than ENDO-DM Type 1 lesions. Findings demonstrate DM in a subset of women with ENDO, which was associated with significant changes in lesion stromal and epithelial nuclear steroid hormone receptor levels, suggestive of disease progression.

Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children

AIMS/HYPOTHESIS

The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children.

METHODS

Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap.

RESULTS

A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up.

CONCLUSIONS/INTERPRETATION

Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.

Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes

In our previous data-driven analysis of evolving patterns of islet autoantibodies (IAb) against insulin (IAA), GAD (GADA), and islet antigen 2 (IA-2A), we discovered three trajectories, characterized according to multiple IAb (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n = 643) with those remaining undiagnosed (n = 1,502). With use of thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlaid onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (P < 0.001), but for IAA dwell times differed only within TR2 (P < 0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has long been appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb-positive children who progressed to type 1 diabetes from those who did not.

Precision medicine in type 1 diabetes

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.

Beta cell and immune cell interactions in autoimmune type 1 diabetes: How they meet and talk to each other

Background

Scope of review

Major conclusions

Environmental Triggering of Type 1 Diabetes Autoimmunity

Type 1 diabetes (T1D) is a chronic autoimmune disease in which pancreatic islet β cells are destroyed by immune cells, ultimately leading to overt diabetes. The progressive increase in T1D incidence over the years points to the role of environmental factors in triggering or accelerating the disease process which develops on a highly multigenic susceptibility background. Evidence that environmental factors induce T1D has mostly been obtained in animal models. In the human, associations between viruses, dietary habits or changes in the microbiota and the development of islet cell autoantibodies or overt diabetes have been reported. So far, prediction of T1D development is mostly based on autoantibody detection. Future work should focus on identifying a causality between the different environmental risk factors and T1D development to improve prediction scores. This should allow developing preventive strategies to limit the T1D burden in the future.