A machine learning approach to predict response to immunotherapy in type 1 diabetes

Fousteri, Georgia; Rodrigues, Ely Montee; Giamporcaro, Gian Maria; Falcone, Marika

doi:10.1038/s41423-020-00594-4

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…T1DM and other types of diabetes caused by autoimmune attacks on the pancreas have also been studied using gene association studies. Fousteri et al [ 159 ] develop a T1DM immunotherapy efficacy prediction tool based on a cell-specific ANN that links genetic and environmental factors to treatment efficacy. Principal component analysis (PCA) and other data mining techniques are used by Xing et al [ 160 ] to establish transcriptional differences as predictors for latent autoimmune diabetes in adults.…”

Section: Ai In Understanding Diabetes Pathophysiologymentioning

confidence: 99%

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Alqahtani

2022

Evidence-Based Complementary and Alternative Medicine

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Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world’s largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

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Section: Ai In Understanding Diabetes Pathophysiologymentioning

confidence: 99%

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Alqahtani

2022

Evidence-Based Complementary and Alternative Medicine

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“…Hyperglycemia in T2D is triggered by both impaired action and secretion of insulin [11], unlike T1D the hallmark of which is a lack of insulin synthesis owing to autoimmune-mediated pancreatic β-cell destruction [12]. Cardinal tissues of the body impacted by heightened insulin resistance (IR) and diminished insulin secretion in T2D include the pancreas, liver, skeletal muscle, and adipose tissue [13].…”

Section: Introductionmentioning

confidence: 99%

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: A bioinformatics analytic workflow

Silva

Demmer

Jönsson

et al. 2022

Preprint

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Introduction: Type 2 diabetes (T2D) has a complex etiology which is not fully elucidated. Identification of gene perturbations and hub genes of T2D may assist in personalizing care. Objectives: We aimed to identify highly perturbed genes and hub genes associated with T2D in different tissues of adult humans via an extensive workflow. Methods: Workflow comprised five sequential steps: systematic review of NCBI GEO database; identification and classification of differentially expressed genes (DEG); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; downstream analyses. Three meta-analytic strategies: random effects model (REM); vote counting approach (VC); p-value combining approach (CA), were applied. Nodes having above average betweenness, closeness, and degree in the network were defined as hub genes. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19 related genes, and Genotype-Tissue Expression profiles. Results: Analysis of 27 eligible microarrays identified 6284 DEG (4592 down-regulated and 1692 up-regulated) within four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Meta-analysis of DEG identified 49, 27, and 8 highly perturbed genes via REM, VC, and CA, respectively, producing a compiled set of 79 highly perturbed (41 down-regulated and 38 up-regulated) genes. The 28 hub genes comprised 13 up-regulated, 9 down-regulated, and 6 predicted genes. Downstream analyses identified enrichments of: shared genes with other diabetes phenotypes; insulin synthesis and action related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways, COVID-19 related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Conclusions: We identified highly perturbed genes and hub genes of T2D and revealed their associations with other diabetes phenotypes and COVID-19 as well as pathophysiological manifestations such as those related to insulin, immunity, and apoptosis. Broader utility of the proposed pipeline is envisaged.

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“…Today, T1D patients can be subdivided into three stages based on the presence of islet-specific AAbs and impaired glucose tolerance: stage 1 T1D, with individuals positive for at least two islet-specific AAbs and no metabolic dysregulation; stage 2 T1D, with individuals who developed impaired glucose tolerance; and stage 3 T1D, with individuals with multiple AAb-positive and fasting hyperglycemia (clinical diabetes) (6,7) A poorly defined interaction between genetic and environmental factors underlies T1D pathogenesis. HLA accounts for the majority of T1D genetic risk, whereas singlenucleotide polymorphisms (SNPs) in non-HLA genes, such as INS, PTPN22, IL2RA, IFIH1, and CTLA4, are considered additional contributing genetic factors (8,9). Recently, several T1D genetic risk scores (GRSs) have been developed based on HLA and non-HLA T1D-risk genes .…”

Section: Introductionmentioning

confidence: 99%

Natural history of type 1 diabetes on an immunodysregulatory background with genetic alteration in B-cell activating factor receptor: A case report

et al. 2022

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The immunological events leading to type 1 diabetes (T1D) are complex and heterogeneous, underscoring the necessity to study rare cases to improve our understanding. Here, we report the case of a 16-year-old patient who showed glycosuria during a regular checkup. Upon further evaluation, stage 2 T1D, autoimmune thrombocytopenic purpura (AITP), and common variable immunodeficiency (CVID) were diagnosed. The patient underwent low carb diet, losing > 8 kg, and was placed on Ig replacement therapy. Anti-CD20 monoclonal antibody (Rituximab, RTX) was administered 2 years after diagnosis to treat peripheral polyneuropathy, whereas an atypical mycobacteriosis manifested 4 years after diagnosis and was managed with prolonged antibiotic treatment. In the fifth year of monitoring, the patient progressed to insulin dependency despite ZnT8A autoantibody resolution and IA-2A and GADA autoantibody decline. The patient had low T1D genetic risk score (GRS = 0.22817) and absence of human leukocyte antigen (HLA) DR3/DR4-DQ8. Genetic analysis identified the monoallelic mutation H159Y in TNFRSF13C, a gene encoding B-cell activating factor receptor (BAFFR). Significant reduced blood B-cell numbers and BAFFR levels were observed in line with a dysregulation in BAFF–BAFFR signaling. The elevated frequency of PD-1+ dysfunctional Tfh cells composed predominantly by Th1 phenotype was observed at disease onset and during follow-up. This case report describes a patient progressing to T1D on a BAFFR-mediated immunodysregulatory background, suggesting a role of BAFF–BAFFR signaling in islet-specific tolerance and T1D progression.

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A machine learning approach to predict response to immunotherapy in type 1 diabetes

Cited by 7 publications

References 15 publications

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: A bioinformatics analytic workflow

Natural history of type 1 diabetes on an immunodysregulatory background with genetic alteration in B-cell activating factor receptor: A case report

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