Relationship between post-IVIG IgG levels and clinical outcomes in Kawasaki disease patients: new insight into the mechanism of action of IVIG

Experience of IVIG Treatment in an Overweight 14-year-old Child With Kawasaki Disease: A Case Report

Kawasaki disease, or mucocutaneous lymph node syndrome, is an acute systemic vasculitis involving small and medium-sized vessels. It can be complicated by varying degrees of cardiac damage, especially coronary artery disease. The disease mainly occurs in children aged < 5 years, with rarer cases in older children and adults. Intravenous immunoglobulin combined with aspirin is the widely accepted treatment regimen in the acute phase, but the dosage recommended by the American Heart Association guidelines is not suitable for heavier children. This article reports the successful management of an overweight 14-year-old child with Kawasaki disease.

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective

INTRODUCTION

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

AREAS COVERED

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

EXPERT OPINION

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

Hygienic behaviors during the COVID-19 pandemic may decrease immunoglobulin G levels: Implications for Kawasaki disease

Background

Due to the coronavirus disease 2019 (COVID-19) pandemic, hygienic behaviors became a new norm since January 2020. The hygiene hypothesis predicts that an excessively hygienic environment may adversely affect human health.

Objective

We quantified the effect of COVID-19 on immunological parameters linked to the hygiene hypothesis.

Methods

We examined age-specific levels of total nonspecific immunoglobulin G (IgG) and IgE in individuals who visited Fukuoka Tokushukai Hospital between 2010 and 2021. Pre-COVID (2010–2019) and COVID (2020–2021) periods were compared.

Results

IgG levels steadily decreased throughout Pre-COVID period. IgG levels fell abruptly from the pre-COVID period to the COVID period in all age groups (P = 0.0271, < 0.3 years; P = 0.0096, 0.3–5 years; P = 0.0074, ≥ 5 years). The declines in IgG in < 0.3 years and that in ≥ 5 years accelerated during the COVID period. IgE levels were seasonal, but did not change noticeably from the pre-COVID to COVID period. IgG levels recorded for patients with Kawasaki disease (KD) (mean 709 mg/dL) were significantly lower than for matched control subjects (826 mg/dL) (P<0.0001).

Discussion

Hygienic behaviors during the COVID-19 outbreak decreased the chance of infection, which may explain the decreases in IgG levels in children and adults. Neonatal IgG declined, possibly because of the decrease in maternal IgG.

Conclusion

Hygienic behaviors decreased the IgG levels in all age groups, from neonates to adults. This downturn in IgG may lead to vulnerability to infections as well as to KD.

Intravenous Immunoglobulins Promote an Expansion of Monocytic Myeloid-Derived Suppressor Cells (MDSC) in CVID Patients

Common variable immunodeficiency disorders (CVID), the most common primary immune deficiency, includes heterogeneous syndromes characterized by hypogammaglobulinemia and impaired antibody responses. CVID patients frequently suffer from recurrent infections and inflammatory conditions. Currently, immunoglobulin replacement therapy (IgRT) is the first-line treatment to prevent infections and aminorate immune alterations in CVID patients. Intravenous Immunoglobulin (IVIg), a preparation of highly purified poly-specific IgG, is used for treatment of immunodeficiencies as well as for autoimmune and inflammatory disorders, as IVIg exerts immunoregulatory and anti-inflammatory actions on innate and adaptive immune cells. To determine the mechanism of action of IVIg in CVID in vivo, we determined the effect of IVIg infusion on the transcriptome of peripheral blood mononuclear cells from CVID patients, and found that peripheral blood monocytes are primary targets of IVIg in vivo, and that IVIg triggers the acquisition of an anti-inflammatory gene profile in human monocytes. Moreover, IVIg altered the relative proportions of peripheral blood monocyte subsets and enhanced the proportion of CD14⁺ cells with a transcriptional, phenotypic, and functional profile that resembles that of monocytic myeloid-derived suppressor cells (MDSC). Therefore, our results indicate that CD14 + MDSC-like cells might contribute to the immunoregulatory effects of IVIg in CVID and other inflammatory disorders.

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the "undiagnosed" types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis.