From Disease and Patient Heterogeneity to Precision Medicine in Type 1 Diabetes

Abstract

Type 1 diabetes (T1D) remains a devastating disease that requires much effort to control. Life-long daily insulin injections or an insulin pump are required to avoid severe complications. With many factors contributing to disease onset, T1D is a complex disease to cure. In this review, the risk factors, pathophysiology and defect pathways are discussed. Results from (pre)clinical studies are highlighted that explore restoration of insulin production and reduction of autoimmunity. It has become clear that treatment responsiveness depends on certain pathophysiological or genetic characteristics that differ between patients. For instance, age at disease manifestation associated with efficacy of immune intervention therapies, such as depleting islet-specific effector T cells or memory B cells and increasing immune regulation. The new challenge is to determine in whom to apply which intervention strategy. Within patients with high rates of insulitis in early T1D onset, therapy depleting T cells or targeting B lymphocytes may have a benefit, whereas slow progressing T1D in adults may be better served with more sophisticated, precise and specific disease modifying therapies. Genetic barcoding and immune profiling may help determining from which new T1D endotypes patients suffer. Furthermore, progressed T1D needs replenishment of insulin production besides autoimmunity reversal, as too many beta cells are already lost or defect. Recurrent islet autoimmunity and allograft rejection or necrosis seem to be the most challenging obstacles. Since beta cells are highly immunogenic under stress, treatment might be more effective with stress reducing agents such as glucagon-like peptide 1 (GLP-1) analogs. Moreover, genetic editing by CRISPR-Cas9 allows to create hypoimmunogenic beta cells with modified human leukocyte antigen (HLA) expression that secrete immune regulating molecules. Given the differences in T1D between patients, stratification of endotypes in clinical trials seems essential for precision medicines and clinical decision making.

A set of circulating microRNAs belonging to the 14q32 chromosomic locus identifies two clinically and phenotypically different subgroups of individuals with recent onset Stage 3 type 1 diabetes

“…However, none of the previous studies have taken an unbiased approach to utilize circulating microRNAs for stratifying individuals with T1DM shortly after disease onset, with the aim of identifying novel disease subgroups. Notably, identifying distinct disease subgroups associated with specific phenotypes may shed light on the heterogeneity of T1DM and potentially aid in the stratification of individuals and their assignment to specific interventional immunotherapy 63 .…”

Suppression of Autoimmune Rheumatoid Arthritis with Hybrid Nanoparticles That Induce B and T Cell Tolerance to Self-Antigen

“…Many if not most autoimmune diseases involve antibodies to self-antigens that mediate disease pathology. − Some of the most promising strategies for treating the cause of these diseases directly or indirectly target either B cells or T cells involved in the production of autoimmune antibodies. ,− B cell depletion via CD20-targeting monoclonal antibody rituximab has been FDA approved to treat rheumatoid arthritis and pemphigus and is being evaluated for treatment of many other autoimmune diseases such as SLE. ,− While remission of symptoms in pemphigus is achieved in most patients, relapses occur when B cells are replenished, requiring additional rounds of treatment. , T cell directed therapies include depletion of T cells at the induction/expansion of tolerogenic Tregs that suppress immune responses. ,, For example, administration of low doses of interleukin 2 to expand Tregs has shown promise in the treatment of systemic lupus erythematosus (SLE) and type I diabetes . Cellular therapies using CD19-CAR T cells to deplete B cells or administering expanded Tregs are also beginning to show promise in clinical trials. − …”

Postprandial glucose‐management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence

“…genomics/genetics, disease history) and abnormal events (e.g. Ramadan, menstrual cycles) 123–125 . These external background factors can be estimated, reported by individuals, automatically captured by wearable devices, or collected from electronic health records and databases.…”