Background: Lockdown is an effective nonpharmaceutical intervention to reduce coronavirus disease 2019 (COVID-19) transmission, but it restricts daily activity. We aimed to investigate the impact of lockdown on pediatric body weight and body mass index (BMI). Methods: The systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Four online databases (EMBASE, Medline, the Cochrane Library and CINAHL) were searched. Results: The pooled results showed that lockdown was associated with significant body weight gain (MD 2.67, 95% CI 2.12–3.23; p < 0.00001). The BMI of children with comorbidities or obesity did not change significantly. The BMI of general population was significantly higher during lockdown than before the pandemic (MD 0.94, 95% CI 0.32–1.56; p = 0.003). However, heterogeneity was high (I² = 84%). Among changes in weight classification, increases in the rates of obesity (OR 1.23, 95% CI 1.10–1.37; p = 0.0002) and overweight (OR 1.17, 95% CI 1.06–1.29; p = 0.001) were reported. Conclusions: Our meta-analysis showed significant increases in body weight and BMI during lockdown among school-age children and adolescents. The prevalence of obesity and overweight also increased. The COVID-19 pandemic worsened the burden of childhood obesity.

Vascular endothelial growth factor (VEGF) is an important factor in mediating the inflammation of Kawasaki disease (KD). The literature regarding the relationship between VEGF and KD is sparse. The purpose of this study was to investigate the correlation of VEGF and KD. In a prospective study of 42 Taiwanese KD patients (18.9 ± 12.2 months, M/F 22/20) treated with intravenous immunoglobulin (IVIG), a series of VEGF levels was measured from the acute to convalescent phases. KD patients were classified into two subgroups with (n =20) and without (n = 22) acute coronary artery lesions (CALs). Control samples were obtained from 30 febrile controls (19.1 ± 13.7 months, M/F 13/17). In KD patients, VEGF levels in the pre-IVIG acute phase were significantly higher than those in the subacute and convalescent phases (both p < 0.001). In KD patients with CALs, VEGF levels significantly increased immediately in the post-IVIG phase (p = 0.039), and then significantly decreased in the subacute phase (p = 0.002). KD patients with acute CALs had higher median VEGF levels than those without acute CALs from acute to convalescent phases. In the subacute phase, KD patients with acute CALs had significantly higher VEGF levels (p = 0.022) than those without acute CALs. Our data show that VEGF did not decrease after IVIG treatment, and increased significantly after IVIG treatment in KD patients with acute CALs in acute phase. VEGF might be related to the complications of CALs in KD patients.

Coronary artery lesions (CAL) are a major complication of Kawasaki disease (KD). The early prediction of CAL enables the medical personnel to apply adequate medical intervention. We collected the serum samples from the KD patients with CAL (n = 32) and those without CAL (n = 31), followed by a global screening with isobaric tagging for relative and absolute quantification (iTRAQ) technology and specific validation with an enzyme-linked immunosorbent assay (ELISA). iTRAQ identified 846 proteins in total in the serum samples, and four candidate proteins related to CAL were selected for ELISA validation as follows: Protein S100-A4 (S100A4), Catalase (CAT), Folate receptor gamma (FOLR3), and Galectin 10 (CLC). ELISA validation showed that the S100A4 level was significantly higher in KD patients with CAL than in those without CAL (225.2 ± 209.5 vs. 143.3 ± 83 pg/mL, p < 0.05). In addition, KD patients with CAL had a significantly lower CAT level than those without CAL (1.6 ± 1.5 vs. 2.7 ± 2.3 ng/mL, p < 0.05). Next, we found that S100A4 treatment on human coronary artery endothelial cells (HCAECs) reduced the abundance of cell junction proteins, which promoted the migration of HCAECs. Further assays also demonstrated that S100A4 treatment enhanced the permeability of the endothelial layer. These results concluded that S100A4 treatment resulted in an incompact endothelial layer and made HCAECs more susceptible to in vitro neutrophil infiltration. In addition, both upregulated S100A4 and downregulated CAT increased the risk of CAL in KD. Further in vitro study implied that S100A4 could be a potential therapeutic target for CAL in KD.

Kawasaki disease (KD) is a leading cause of acquired coronary vasculitis in children and remains a critical diagnostic challenge among febrile pediatric patients. To support frontline pediatricians with a more objective diagnostic tool, we developed and implemented KDpredictor, a machine learning-based model for KD risk identification. KDpredictor leverages only the routine blood test features, including complete blood count with differential count, C-reactive protein, and alanine aminotransferase. It also takes the lead in using age-calibrated eosinophil, platelet, and hemoglobin results. Trained using the light gradient boosting machine algorithm on clinical data from 1,927 KD cases and 45,274 febrile controls, KDpredictor achieved strong performance metrics (auROC: 95.7%, auPRC: 72.4%, recall: 0.89) on a reserved test set, outperforming previous models by at least 3% in auROC and 39.3% in auPRC. Additional explainable AI analyses revealed that several top predictive features in KDpredictor are consistent with prior clinical findings. We also evaluated KDpredictor on three independent cohorts collected in East Asia (Taiwan and China) during the COVID-19 period. KDpredictor achieves recall values of 90.9%, 83.7%, and 91.7% on KD samples identified in three independent medical centers, respectively, indicating its applicability across independent clinical settings. In summary, KDpredictor demonstrates robust generalizability in KD risk identification across populations by using only standard blood samples independent of clinical symptoms. KDpredictor is freely available at https://cosbi.ee.ncku.edu.tw/KD_under7/.