Epitope-based precision immunotherapy of Type 1 diabetes

Antigen-specific immunotherapies (ASITs) address important clinical needs in treating autoimmune diseases. However, Type 1 diabetes is a heterogeneous disease wherein patient characteristics influence responsiveness to ASITs. Targeting not only disease-relevant T cell populations, but also specific groups of patients using precision medicine is a new goal toward achieving effective treatment. HLA-restricted peptides provide advantages over protein as antigens, however, methods for profiling antigen-specific T cells need to improve in sensitivity, depth, and throughput to facilitate epitope selection. Delivery approaches are highly diverse, illustrating the many ways relevant antigen-presenting cell populations and anatomical locations can be targeted for tolerance induction. The role of persistence of antigen presentation in promoting durable antigen-specific tolerance requires further investigation. Based on the outcome of ASIT trials, the field is moving toward using patient-specific variations to improve efficacy, but challenges still lie on the path to delivering more effective and safer treatment to the T1D patient population.

Type 1 diabetes and inborn errors of immunity: Complete strangers or 2 sides of the same coin?

Type 1 diabetes (T1D) is a polygenic disease and does not follow a mendelian pattern. Inborn errors of immunity (IEIs), on the other hand, are caused by damaging germline variants, suggesting that T1D and IEIs have nothing in common. Some IEIs, resulting from mutations in genes regulating regulatory T-cell homeostasis, are associated with elevated incidence of T1D. The genetic spectrum of IEIs is gradually being unraveled; consequently, molecular pathways underlying human monogenic autoimmunity are being identified. There is an appreciable overlap between some of these pathways and the genetic variants that determine T1D susceptibility, suggesting that after all, IEI and T1D are 2 sides of the same coin. The study of monogenic IEIs with a variable incidence of T1D has the potential to provide crucial insights into the mechanisms leading to T1D. These insights contribute to the definition of T1D endotypes and explain disease heterogeneity. In this review, we discuss the interconnected pathogenic pathways of autoimmunity, β-cell function, and primary immunodeficiency. We also examine the role of environmental factors in disease penetrance as well as the circumstantial evidence of IEI drugs in preventing and curing T1D in individuals with IEIs, suggesting the repositioning of these drugs also for T1D therapy.

Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes

An unbiased and replicable profiling of type 1 diabetes (T1D)-specific circulating immunome at disease onset has yet to be identified due to experimental and patient selection limitations. Multicolor flow cytometry was performed on whole blood from a pediatric cohort of 107 patients with new-onset T1D, 85 relatives of T1D patients with 0-1 islet autoantibodies (pre-T1D_LR), 58 patients with celiac disease or autoimmune thyroiditis (CD_THY) and 76 healthy controls (HC). Unsupervised clustering of flow cytometry data, validated by a semi-automated gating strategy, confirmed previous findings showing selective increase of naïve CD4 T cells and plasmacytoid DCs, and revealed a decrease in CD56brightNK cells in T1D. Furthermore, a non-selective decrease of CD3+CD56+ regulatory T cells was observed in T1D. The frequency of naïve CD4 T cells at disease onset was associated with partial remission, while it was found unaltered in the pre-symptomatic stages of the disease. Thanks to a broad cohort of pediatric individuals and the implementation of unbiased approaches for the analysis of flow cytometry data, here we determined the circulating immune fingerprint of newly diagnosed pediatric T1D and provide a reference dataset to be exploited for validation or discovery purposes to unravel the pathogenesis of T1D.

Association between new markers of cardiovascular risk and hepatic insulin resistance in those at high risk of developing type 2 diabetes

AIM/HYPOTHESIS

Hepatic insulin resistance (HIR) is considered to be an independent predictor of metabolic disorders and plays an important role in systemic inflammation, which contributes to abnormalities in cardiovascular disease (CVD) risk factors. The aim of this study was to investigate the relationship between HIR and new markers of cardiovascular risks, including leptin/adiponectin ratio (L/A), lipoprotein(a) [Lp(a)], and tumor necrosis factor alpha (TNF-α), at comparable whole body insulin sensitivity in non-diabetic individuals with or without CVD and at high risk of developing type 2 diabetes.

METHODS

The HIR index, L/A, Lp(a), and TNF-α were measured in 50 participants with CVD and in 200 without CVD (1:4 ratio). These were also matched for the homeostatic model assessment for insulin resistance (HOMA-IR) and Matsuda-insulin sensitivity index (ISI) in an observational study design.

RESULTS

The HIR index (1.52 ± 0.14 vs. 1.45 ± 0.17, p < 0.02), L/A (3.22 ± 3.10 vs. 2.09 ± 2.27, p < 0.004), and levels of Lp(a) (66.6 ± 49.5 vs. 37.9 ± 3 6.8 mg/dL, p < 0.0001) and TNF-α (18.9 ± 21.8 vs. 5.4 ± 7.1 pg/mL, p < 0.0001) were higher in those with CVD than those without CVD. HOMA-IR and ISI were not significantly different (p = 0.88 and p = 0.35, respectively). The HIR index was directly correlated with L/A (r = 0.41, p < 0.0001), Lp(a) (r = 0.20, p < 0.002), TNF- α (r = 0.14, p < 0.03), and diastolic blood pressure (DBP) (r = 0.13, p < 0.03). The stepwise model analysis showed that L/A, Lp(a), and TNF-α explained about 20% of the variation in the HIR indices of all the participants (p < 0.02).

CONCLUSIONS/INTERPRETATIONS

Our results suggest a positive association between HIR and new markers of cardiovascular risk [L/A, Lp(a), and TNF- α] at comparable whole body insulin sensitivity in those with or without CVD and at high risk of developing type 2 diabetes.