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.

Islet autoantibodies are markers found in the blood when insulin-producing cells in the pancreas become damaged and can be used to predict future development of type 1 diabetes. We evaluated published literature to determine whether characteristics of islet antibodies (type, levels, numbers) could improve prediction and help understand differences in how individuals with type 1 diabetes respond to treatments. We found existing evidence shows that islet autoantibody type and number are most useful to predict disease progression before diagnosis. In addition, the age when islet autoantibodies first appear strongly influences rate of progression. These findings provide important information for patients and care providers on how islet autoantibodies can be used to understand future type 1 diabetes development and to identify individuals who have the potential to benefit from intervention or prevention therapy.

Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review.

BACKGROUND: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. METHODS: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. RESULTS: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. CONCLUSIONS: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.

Type 1 diabetes (T1D) is a condition that results from the destruction of a type of cell in the pancreas that produces the hormone insulin, leading to lifelong dependence on insulin injections. T1D prevention remains a challenging goal, largely due to the immense variability in disease processes and progression. Therapies tested to date in medical research settings (clinical trials) work only in a subset of individuals, highlighting the need for more tailored prevention approaches. We reviewed clinical trials of therapies targeting the disease process in T1D. While the overall quality of trials was high, studies testing individual features affecting responses to treatments were low. This review reveals an important need to carefully plan high-quality analyses of features that affect treatment response in T1D, to ensure that tailored approaches may one day be applied to clinical practice.

The effect of age on longitudinal measures of beta cell function and insulin sensitivity during the progression of early stage type 1 diabetes.

AIM/HYPOTHESIS: The risk of progressing from autoantibody positivity to type 1 diabetes is inversely related to age. Separately, whether age influences patterns of C-peptide loss or changes in insulin sensitivity in autoantibody-positive individuals who progress to stage 3 type 1 diabetes is unclear. METHODS: Beta cell function and insulin sensitivity were determined by modelling of OGTTs performed in 658 autoantibody-positive participants followed longitudinally in the Diabetes Prevention Trial-Type 1 (DPT-1). In this secondary analysis of DPT-1 data, time trajectories of beta cell function and insulin sensitivity were analysed in participants who progressed to type 1 diabetes (progressors) to address the impact of age on patterns of metabolic progression to diabetes. RESULTS: Among the entire DPT-1 cohort, the highest discriminant age for type 1 diabetes risk was 14 years, with participants aged <14 years being twice as likely to progress to type 1 diabetes as those aged ≥14 years. At study entry, beta cell glucose sensitivity was impaired to a similar extent in progressors aged <14 years and progressors aged ≥14 years. From study entry to stage 3 type 1 diabetes onset, beta cell glucose sensitivity and insulin sensitivity declined in both progressor groups. However, there were no significant differences in the yearly rate of decline in either glucose sensitivity (-13.7 [21.2] vs -11.9 [21.5] pmol min-1 m-2 [mmol/l]-1, median [IQR], p=0.52) or insulin sensitivity (-22 [37] vs -14 [40] ml min-1 m-2, median [IQR], p=0.07) between progressors aged <14 years and progressors aged ≥14 years. CONCLUSIONS/INTERPRETATION: Our data indicate that during progression to stage 3 type 1 diabetes, rates of change in declining glucose and insulin sensitivity are not significantly different between progressors aged <14 years and progressors aged ≥14 years. These data suggest there is a predictable course of declining metabolic function during the progression to type 1 diabetes that is not influenced by age.

Not so sweet and simple: impacts of SARS-CoV-2 on the ß cell.

The link between COVID-19 infection and diabetes has been explored in several studies since the start of the pandemic, with associations between comorbid diabetes and poorer prognosis in patients infected with the virus and reports of diabetic ketoacidosis occurring with COVID-19 infection. As such, significant interest has been generated surrounding mechanisms by which the virus may exert effects on the pancreatic ß cells. In this review, we consider possible routes by which SARS-CoV-2 may impact ß cells. Specifically, we outline data that either support or argue against the idea of direct infection and injury of ß cells by SARS-CoV-2. We also discuss ß cell damage due to a "bystander" effect in which infection with the virus leads to damage to surrounding tissues that are essential for ß cell survival and function, such as the pancreatic microvasculature and exocrine tissue. Studies elucidating the provocation of a cytokine storm following COVID-19 infection and potential impacts of systemic inflammation and increases in insulin resistance on ß cells are also reviewed. Finally, we summarize the existing clinical data surrounding diabetes incidence since the start of the COVID-19 pandemic.