Abstract
Kawasaki disease (KD) is the leading cause of acquired cardiovascular disease in children and is characterized by intense immune activation and platelet dysfunction. High platelet reactivity (HPR) is increasingly recognized as a biological basis of aspirin resistance (AR), which may increase the risk of adverse coronary outcomes, including coronary artery aneurysms (CAA). However, the hematologic dynamics and genetic determinants underlying AR in KD remain unclear. In this study, the association between AR and CAA was assessed using chi-square analysis. We compared platelet parameters between KD aspirin-resistant (KD-AR) and KD non-aspirin-resistant (KD-NAR) patients across different disease phases using linear mixed-effects models (LMM). Baseline complete blood count (CBC) derived inflammatory indices, including the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR), were evaluated using restricted cubic spline (RCS) and receiver operating characteristic (ROC) analyses. Integrated transcriptomic and expression quantitative trait loci (eQTL) analyses were performed to identify candidate genetic factors associated with the KD-AR phenotype. The results showed that AR was significantly associated with CAA formation. LMM showed significant phase-dependent changes in platelet parameters, with distinct longitudinal trajectories between KD-AR and KD-NAR patients. Between-group differences were mainly observed during the subacute phase (D7–14), when KD-AR patients showed lower platelet count (PLT) and plateletcrit (PCT), but higher platelet distribution width (PDW) and platelet large cell ratio (PLCR). Baseline SII, PLR, and NLR were significantly elevated in KD-AR patients (all P < 0.001). RCS analyses demonstrated significant overall associations between these indices and AR risk (all Poverall < 0.001). ROC analyses showed moderate discrimination for SII (AUC = 0.702) and NLR (AUC = 0.722), whereas PLR showed lower performance (AUC = 0.626). MBP was consistently upregulated in HPR-associated samples, and eQTL integration identified MBP/rs8090438 as a candidate variant linked to KD-AR. These findings suggest that AR in KD represents a multifactorial phenotype involving immune-driven platelet dysregulation and genetic susceptibility. Baseline inflammatory indices, particularly NLR and SII, may assist in early identification of KD patients at increased likelihood of AR.
1 Introduction
Kawasaki disease (KD), a systemic inflammatory disorder predominantly affecting children under 5 years of age, exhibits a significantly higher incidence in Asian populations (). As the leading cause of acquired cardiovascular disease in childhood, KD may progress to coronary artery aneurysms (CAA) in approximately 25% of untreated cases (). Occlusive thrombosis secondary to CAA represents the primary contributor to KD mortality (), closely associated with high platelet reactivity (HPR) (). HPR is defined as a state in which platelets remain in relatively activated and prone to aggregation despite ongoing antiplatelet therapy. this condition reflects incomplete suppression of key platelet activation pathways (particularly adenosine diphosphate (ADP) or arachidonic acid (AA) signaling), which may cause clopidogrel resistance or aspirin resistance (AR) (). Patients with HPR exhibit persistently high platelet activation and aggregation potential, which is associated with an increased risk of ischemic complications, particularly in KD patients with CAA.
Aspirin, which possesses dual antiplatelet and anti-inflammatory properties, is indispensable for preventing thrombosis and vascular injury induced by KD. Although there is currently no evidence to support the use of moderate-to-high doses of aspirin during the acute febrile phase to mitigate the progression of CAA, the American Heart Association (AHA) recommended low-dose aspirin for a minimum of 6–8 weeks as a fundamental therapeutic approach to prevent coronary thrombosis and related major cardiovascular events (). Currently, the efficacy of antithrombotic therapy for KD has been evaluated primarily through observational studies and small-scale trials (; ; ). Historically, the therapeutic benefits of intravenous immunoglobulin (IVIG) have been increasingly emphasized, often overshadowing the issue of AR (). A Chinese clinical cohort study reported an incidence of AR in KD patients ranging from 8.05% to 9.75% (). While few studies have directly quantified the relationship between AR and clinical adverse events, long-term follow-up data indicated that CAA resulting from childhood KD can persist into adulthood, leading to ischemic heart disease and acute coronary syndromes independent of traditional atherosclerotic mechanisms (). Consequently, identifying factors contributing to AR in KD patients undergoing long-term therapy is critical for mitigating cardiovascular complications.
Emerging evidence highlights the prognostic potential of platelet parameters in monitoring antiplatelet therapy efficacy (; ). Routinely measured platelet parameters, including platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT) and platelet large cell ratio (PLCR) are readily obtainable from complete blood counts (CBC). These parameters and their derivative biomarkers have been shown to reflect platelet activation status and therapeutic responsiveness to antiplatelet therapy (; ; ; ). Cost-effectiveness and widespread availability guarantee their clinical utility as indirect indicators of systemic inflammation, with established applications in cardiovascular diseases, thrombotic disorders, and chronic inflammatory conditions including infections and autoimmune diseases (; ; ). From the perspective of KD, recent studies have focused on leveraging inflammation-related hematological ratios, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), the systemic immune-inflammation index (SII, platelet count × neutrophil count/lymphocyte count), to predict IVIG resistance in the acute stage (; ). It has been reported that chronic inflammation (e.g., active atherosclerosis or immune-stimulated states) enhances platelet reactivity and promotes thromboxane A2 (TXA2) production via inflammatory cytokines, thereby reducing the clinical efficacy of aspirin (). Therefore, we aim to further investigate whether CBC-derived inflammatory biomarkers can serve as an early screening tool for AR in KD.
Simultaneously, accumulating genetic studies increasingly implicate hereditary factors as considerable determinants of AR (), and single nucleotide polymorphisms (SNPs) have been reported as critical contributors to this phenotype (; ; ; ). These genetic variants may impair drug bioavailability or target engagement, leading to suboptimal outcomes. Identifying SNPs through pharmacogenomic profiling could enable personalized dosing strategies, optimizing aspirin benefits in KD. Thus, exploring AR-related genetic markers in KD patients is also one of the objectives of this study.
These findings have spurred our exploration of KD in two interrelated domains: (1) the specific platelet parameters or other CBC-derived biomarker profiles associated with AR phenotype, and (2) the identification of genetic biomarkers correlated with AR.
2 Methods
2.1 Subjects
Based on the Women and Children’s Medical Center affiliated with Guangzhou Medical University from 2014 to 2021, our center accepted a total of 1,856 KD patients. All the subjects belonged to the Han ethnic group native to Southern China. The diagnostic criteria for KD were all following the guidelines released by the AHA in 2017 (). Review medical records to make sure that all KD patients were treated with the standard regimen of aspirin (30–50 mg/kg/day) and a single intravenous dose of IVIG (2 g/kg) within the first 10 days from the beginning of fever.
During the study period, platelet aggregation test was standard practice at our institution to perform for all KD patients in the absence of specific contraindications. After excluding patients with follow-up visits and those with missing platelet aggregation test data, a total of 1,210 KD patients were included. The determination of AR in patients with KD should be based on the outcomes of platelet aggregation assays. AR was defined as a mean aggregation of ≥70% with 10 μM ADP and a mean aggregation of ≥20% with 0.5 mM AA (). According to this criterion, a cohort of 1,210 KD patients was stratified into two groups: 738 KD aspirin-non-resistant (KD-NAR) patients and 472 KD aspirin-resistant (KD-AR) patients. We will analyze and compare platelet parameters in KD-AR and KD-NAR at different stages of KD, and further explore the manifestation of CBC-derived inflammatory biomarkers in AR at the acute phase of KD (Figure 1).
FIGURE 1
Furthermore, 1435 healthy controls (HCs) were included to calculate the association between rs8090438 and KD susceptibility (Supplementary Table S1). HCs were randomly selected from children undergoing routine physical examinations and were matched with KD patients based on gender and age.
The study conformed to the Declaration of Helsinki and was performed with the approval of the Ethics Committee of the Women and Children’s Medical Center affiliated with Guangzhou Medical University (approval numbers: 2014073009, 2018052702, 2021093A01). Prior to the study, informed consent was acquired from the legal guardian of each participant.
2.2 Platelet aggregation
For KD patients, venous blood was drawn and placed in tubes (2–4 mL) with anticoagulant citrate dextrose solution (ACD), followed by treatment with 75 nM prostaglandin E1 (PGE1, Catalogue No: HY-B0131, MEC) to prevent platelet activation. Blood samples were collected 3 days after aspirin administration. The final aspirin dose was administered within 1–24 h prior to blood collection. Room-temperature blood samples were processed within 1 h of collection.
2.3 Platelet parameters examination
For each patient, 2 mL of venous blood was collected into collection tubes containing Dipotassium EDTA (1.5 mg/mL) for CBC and immature platelets testing. CBC was analyzed within 2 h of sampling by an automatic blood cell counter (Sysmex XE-5000, Sysmex, Kobe, Japan). The time points for monitoring CBC were as follows: before aspirin treatment (baseline), 7–14 days after aspirin treatment (the subacute phase of KD), and 28–56 days after aspirin treatment (the recovery phase of KD).
2.4 Data collection
The mRNA expression profiling microarray data were sourced from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nih.gov/geo). This database is publicly available and is exempt from ethical review. A search of the GEO database was conducted using the keywords “HPR” and “AR” to identify mRNA expression profiling microarray datasets pertinent to platelet reactivity. In accordance with the study objectives, this research incorporated two datasets, GSE66426 and GSE32226 (Figure 5A), for further analysis. GSE66426 includes transcriptomic profiles from aspirin-treated cardiovascular patients stratified by platelet reactivity, while GSE32226 comprises expression data from coronary artery disease patients receiving antiplatelet therapy with documented platelet response phenotypes. These datasets were selected as established human models of platelet hyperreactivity. Each dataset was utilized to identify differentially expressed genes, followed by an intersection analysis.
Patients' clinical information was acquired through a review of the medical record system.
2.5 Identification of differentially expressed genes (DEGs)
DEG analysis was conducted separately within each dataset using the GEO2R online tool based on the limma package. Given the use of different microarray platforms, expression matrices were not merged across datasets. DEG was identified with P < 0.05 of fold changes (FC) > 1.2 as the significance thresholds for initial exploratory screening. Genes consistently differentially expressed in both datasets were retained for downstream analyses to enhance robustness and reduce platform-specific bias. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform functional enrichment analyses, including analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Related diagrams were performed by R package “ggplot2”.
2.6 Analysis of functional expression quantitative trait loci (eQTL)
205 eQTLs were downloaded from GTEx (https://www.gtexportal.org/home/). eQTLs was evaluated using VannoPortal (http://mulinlab.org/vportal) and QTLbase (http://www.mulinlab.org/qtlbase). The linkage disequilibrium (LD) blocks were calculated among the East Asian population (“EAS”), Ad Mixed American population (“AMR”), and European population (“EUR”), utilizing the R package “LDlinkR”.
2.7 Extracting DNA and genotyping polymorphism
Genomic DNA was extracted from peripheral blood mononuclear cells (PBMCs) using the TIANamp Blood DNA Kit (Tiangen Biotech, Beijing, China). DNA concentration and quality were assessed prior to genotyping.
Genotyping of rs8090438 was performed using the Sequenom MassARRAY iPLEX Gold system (Agena Bioscience, San Diego, CA, USA). In addition, quantitative polymerase chain reaction (qPCR) was used solely for DNA quality assessment. qPCR reactions were conducted in 384-well plates using the ABI QuantStudio 6 Real-Time PCR System (Thermo Fisher Scientific, USA).
The PCR conditions consisted of an initial denaturation at 95 °C for 5 min, followed by 45 cycles of denaturation at 95 °C for 15 s and annealing/extension at 60 °C for 1 min.
2.8 Statistical analysis
Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA), R software version 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria), GraphPad Prism version 9.5 (GraphPad Software, San Diego, CA, USA), PLINK version 1.9 (Massachusetts General Hospital, Boston, MA, USA), and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
Continuous variables were expressed as mean ± standard deviation (SD) for normally distributed data and as median with interquartile range (IQR) for skewed distributions. Categorical variables were presented as frequencies or percentages. Normality of continuous variables was assessed using the Shapiro-Wilk test. Between-group comparisons were conducted using the two-tailed Student’s t test for normally distributed variables and the Mann-Whitney U test for non-normally distributed variables.
For patients with multiple transthoracic echocardiography (TTE) examinations, the record documenting the most severe CAA findings was included for statistical analysis. The chi-square test was used to calculate the association between CAA and AR. Missing data were primarily observed in baseline CBC variables and follow-up platelet measurements (Figure 1). Baseline characteristics were compared between patients with and without available CBC data to assess potential selection bias.
Longitudinal changes of five platelet parameters (PLT, PDW, MPV, PCT, and PLCR) across three disease phases (D0, day before aspirin treatment, the acute phase; D7-14, 7–14 days after aspirin administration, the subacute phase; D28-56, 28–56 days after aspirin administration, the convalescent phase) were analyzed using linear mixed-effects models (LMM) by SPSS. Time and group (KD-NAR vs. KD-AR) were specified as fixed effects, and subject was included as a random effect to account for within-subject correlation. An unstructured covariance matrix was applied for repeated measures, and parameters were estimated using restricted maximum likelihood (REML). LMMs are generally considered robust to incomplete repeated measurements under the missing at random (MAR) assumption, thereby allowing inclusion of all available data without formal imputation. Post hoc simple effects were examined using Bonferroni-adjusted pairwise comparisons of estimated marginal means (EMMs) within each phase. The primary inference was based on the time × group interaction in the LMM.
Restricted cubic spline (RCS) analyses with four knots were performed to explore potential nonlinear associations of SII, PLR, and NLR with the risk of AR by R software. Sensitivity analyses using alternative numbers of knots (knot = 3/4/5) yielded consistent effect patterns, supporting the robustness of the spline models. Receiver operating characteristic (ROC) curve analyses were performed using SPSS to assess the discriminative performance of each CBC-derived inflammatory biomarker for distinguishing KD-AR from KD-NAR patients. The area under the ROC curve (AUC) and corresponding 95% confidence intervals (CIs) were calculated. AUC estimates were derived from univariable logistic regression models.
Genetic association analyses were conducted using PLINK and SAS. Hardy-Weinberg equilibrium (HWE) was assessed, with P > 0.05 indicating acceptable genotyping quality. Associations between the rs8090438 polymorphism and AR, as well as susceptibility to KD, were evaluated under multiple genetic models, including the allelic model (ALLELIC), the 2df genotypic model (GENO), and the co-dominant model (CODOM), additive model (ADD), dominant model (DOM) and recessive model (REC). A two-sided P < 0.05 was considered statistically significant. False discovery rate (FDR) correction was applied to adjust the P values for multiple testing, with PFDR < 0.01 considered statistically significant.
3 Results
3.1 General characteristics
General information of KD patients was collected including the age of the disorder onset, gender and coronary artery outcomes from TTE during visits (Table 1). Coronary artery outcomes of KD patients were categorized according to their Z score: no coronary artery damage (Z < 2), coronary artery dilation (2 < Z < 2.5), small CAA (2.5 ≤ Z < 5), medium CAA (5 ≤ Z < 10, and absolute dimension <8 mm), and giant CAA (Z ≥ 10, or absolute dimension ≥8 mm) (). Table 1 indicated that the KD-AR and KD-NAR cohorts exhibited pathogenics characteristics consistent with those of general KD patients, notably a higher prevalence among males under 5 years of age. The incidence of CAA was relatively consistent with that reported in external general KD cohorts (). The chi-square test results indicated that the AR was significantly associated with the development of CAA (P < 0.05) (Figure 2).
TABLE 1
| Characteristic | KD-NAR (n = 738) | KD-AR (n = 472) | ||
|---|---|---|---|---|
| (n) | (%) | (n) | (%) | |
| Total | 738 | 100.00 | 472 | 100.00 |
| Gender | ||||
| Male | 497 | 67.34 | 301 | 63.77 |
| Female | 241 | 32.66 | 171 | 36.23 |
| Age | ||||
| mean ± SD | 24.00 ± 20.27 | 26.87 ± 21.03 | ||
| ≤5 years-old | 708 | 95.93 | 451 | 95.55 |
| >5 years-old | 30 | 4.07 | 21 | 4.45 |
| Coronary artery outcomes | ||||
| Giant aneurysm | 25 | 3.39 | 23 | 4.87 |
| Medium aneurysm | 55 | 7.45 | 24 | 5.08 |
| Small aneurysm | 50 | 6.78 | 66 | 13.98 |
| Transient dilation | 67 | 9.08 | 71 | 15.04 |
| No dilation | 541 | 73.31 | 288 | 61.02 |
General Characteristics distribution in KD-AR and KD-NAR.
Abbreviations: KD = Kawasaki disease, KD-AR = KD aspirin-resistant patients, KD-NAR = KD aspirin-non-resistant patients, SD = standard deviation.
FIGURE 2
3.2 Longitudinal changes in platelet parameters between KD-NAR and KD-AR groups based on linear mixed-effects models (LMM)
Linear mixed-effects models (LMM) were used to assess longitudinal changes in platelet parameters (PLT, MPV, PDW, PCT, and PLCR) across three disease phases (D0, D7-14, and D28-56) between the KD-NAR and KD-AR groups. No significant differences were observed in baseline characteristics in terms of sex distribution, age, or coronary artery outcomes between patients with and without available baseline CBC data (all P > 0.05) (Supplementary Table S11). All available repeated measurements were included in the analyses.
3.2.1 Overall fixed effects
The LMM demonstrated significant main effects of phase for all five platelet parameters, including PLT (F = 41.287, P < 0.001), MPV (F = 3.098, P = 0.047), PDW (F = 25.002, P < 0.001), PCT (F = 29.360, P < 0.001), and PLCR (F = 27.808, P < 0.001). A significant main effect of aspirin responsiveness was observed for PLT (F = 13.136, P < 0.001) and PCT (F = 12.621, P < 0.001), whereas no significant group effect was detected for MPV, PDW, or PLCR.
Notably, significant phase × group interactions were identified for PLT (F = 38.829, P < 0.001), PDW (F = 8.275, P < 0.001), PCT (F = 29.839, P < 0.001), and PLCR (F = 10.269, P < 0.001). In contrast, no significant interaction effect was observed for MPV (F = 2.352, P = 0.097) (Figures 3A–E; Supplementary Table S12A).
FIGURE 3
3.2.2 Bonferroni-adjusted simple effects
Bonferroni-adjusted pairwise comparisons demonstrated that between-group differences were primarily observed during the subacute phase (D7-14) (Figures 3A–E; Supplementary Table S12C). At this stage, KD-AR patients showed lower PLT (β = −200.613, 95% CI: -251.111 to −150.114, P < 0.001) and lower PCT (β = −0.167, 95% CI: -0.211 to −0.122, P < 0.001), but higher PDW (β = 0.805, 95% CI: 0.357 to 1.253, P < 0.001) and higher PLCR (β = 3.005, 95% CI: 1.096 to 4.914, P = 0.002). No significant between-group differences were observed at D0 or D28–56 (all P > 0.05).
Although the interaction for MPV was not statistically significant (F = 2.352, P = 0.097), a modest increase in MPV was observed in KD-AR patients at D7–14 (β = 0.350, 95% CI: 0.117 to 0.582, P = 0.003).
3.3 Association and discriminative performance of SII, PLR and NLR for early identification of AR in KD patients
In this study, we compared three CBC-derived biomarkers, including SII, PLR and NLR among AR and NAR groups in patients with KD inflammatory condition to further explore whether these indexes have potential to early identify AR phenotype.
SII, PLR and NLR were calculated prior to the initial aspirin treatment. All three indices were significantly higher in the KD-AR group compared with the KD-NAR group (all P < 0.001) (Table 2; Figures 4A–C). Restricted cubic spline (RCS) models with four knots (df = 3) demonstrated significant overall associations between SII, PLR, NLR and AR risk (all Poverall < 0.001) (Figures 4D–F). PLR exhibited a linear correlation with AR (Pnonlinear = 0.660) (Figure 4E). Both SII and NLR demonstrated evidence of a non-linear association (Pnonlinear < 0.05) (Figures 4D,F). Sensitivity analyses using alternative knot specifications (knot = 3/4/5) yielded consistent overall associations, and the general shape of the exposure-response curves remained stable (Supplementary Figure S1), indicating robustness of the findings.
TABLE 2
| Index | KD-NAR (n = 472) | KD-AR (n = 88) | P |
|---|---|---|---|
| SII | 711(401–1221) | 1222(817–1722) | <0.0001 |
| PLR | 101.72(73.65–137.13) | 128.42(86.20–179.46) | <0.001 |
| NLR | 1.87(1.05–3.03) | 3.05(2.20–4.83) | <0.0001 |
Comparison of the NLR, PLR and SII between KD-AR and KD-NAR groups before aspirin treatment.
The data are presented as the median with IQR., Abbreviation; SII = the systemic immune-inflammation index, PLR = platelet-to-lymphocyte ratio, NLR = neutrophil-to-lymphocyte ratio, KD-AR = KD aspirin-resistant patients, KD-NAR = KD aspirin-non-resistant patients, IQR = interquartile range. Bolded text indicates a significant P value.
FIGURE 4
To assess discriminative performance, receiver operating characteristic (ROC) curve analyses were conducted. SII and NLR demonstrated moderate ability to distinguish KD-AR from KD-NAR patients (SII: AUC = 0.702, 95% CI: 0.647–0.758, P < 0.001; NLR: AUC = 0.722, 95% CI: 0.670–0.775, P < 0.001) (Figure 4G; Supplementary Table S13). In contrast, PLR exhibited lower discriminative performance (AUC = 0.626, 95% CI: 0.559–0.692, P < 0.001). Among the three indices, NLR showed the highest discriminative capacity.
Collectively, these findings indicate that elevated baseline SII and NLR levels are associated with increased AR risk and demonstrate moderate discriminative ability.
3.4 The process of DEG identification
The overall workflow of DEG identification was summarized in Figure 5A. After systematic screening of the GEO database, datasets GSE66426 and GSE32226 related to AR were included for subsequent analysis (Figure 5A; Supplementary Table S2). Differential expression analysis identified multiple DEGs in each dataset (Supplementary Tables S3-S4). Intersection analysis of the two DEG sets yielded 38 shared genes, among which 26 genes showed concordant directions of regulation (either upregulated or downregulated) in both datasets (Supplementary Table S5). Functional enrichment analysis of these 26 consistently regulated genes revealed significant enrichment in pathways related to the mitogen-activated protein kinase (MAPK) cascade, intracellular immune responses, and oxidative stress-related processes (Supplementary Table S6).
FIGURE 5
3.5 Pathogenic potential of MBP/rs8090438 in cohort with HPR
Among the 26 genes showing concordant differential expression across both datasets, MBP was identified as a consistently upregulated gene (Supplementary Table S5). To further explore genetic variants potentially regulating MBP expression, eQTLs associated with MBP were retrieved from GTEx, yielding a total of 205 variants (Supplementary Table S7). Linkage disequilibrium (LD) analysis identified four major LD blocks in the “EAS” population (Figure 5D; Supplementary Table S8). To facilitate downstream annotation, one representative SNP from each LD block was selected based on LD independence (Supplementary Table S9). The variant rs8090438 was located within block I and exhibited consistent LD patterns across three populations.
Functional annotation using VannoPortal and QTLbase indicated that rs8090438 displayed high evolutionary conservation, supported by mamPhyloP (Phred Score 11.9902) and verPhyloP (Phred Score 11.6526). In addition, regulatory potential was suggested by Eigen_PC (Score 0.3813, Phred Score 12.4600) and fitCons (Score 0.0537, Phred Score 1.8046). The distribution of rs8090438 was detected in platelet-derived tissue, including blood cells and pluripotent stem cells (Figure 5B). Vannoportal noted that rs8090438 T allele potentially increased the binding affinity of several proteins including NR4A1 and RXRA, which were involved in immune and metabolic regulation, altering responses to environmental stimuli or metabolic states (Supplementary Table S10).
Epigenomic annotation showed that rs8090438 was located in open chromatin regions with H3K27ac enrichment in heart tissue (Figure 5E). Chromatin interaction analysis based on Hi-C data from endothelial and lymphoblastoid cells revealed potential long-range interactions between rs8090438 and distal genomic regions (Figure 5C).
3.6 Stratification analysis of MBP/rs8090438 about KD susceptibility and KD-AR correlation
Stratification analysis was first performed to evaluate the association between MBP/rs8090438 and KD susceptibility. No significant differences in genotype distribution were observed between KD patients and controls (Supplementary Table S1).
The association between MBP/rs8090438 and AR was further evaluated in 51 KD-AR patients and 401 KD-NAR patients. Genetic association analyses were conducted under multiple inheritance models, including the allelic model (ALLELIC), 2df genotypic model (GENO), co-dominant model (CODOM), additive model (ADD), dominant model (DOM) and recessive model (REC). Significant associations between MBP/rs8090438 and AR were observed across several genetic models, with the T allele conferring an increased risk of AR (Table 3). Notably, these associations remained statistically significant after FDR correction (PFDR < 0.01), indicating robustness against multiple testing.
TABLE 3
| SNP | Gene | A1/A2 | Freq_A1 | Freq_A2 | HWE | Model | Genotype | AFF | UNAFF | OR | POR | PFDR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs8090438 | MBP | T/A | 0.25 | 0.36 | 0.807 | ALLELIC | T/A | 37/65 | 198/604 | 1.74(1.12–2.68) | 0.012 | 0.015 |
| GENO | 0.0043 | 0.0072 | ||||||||||
| CODOM | AA | 17 | 229 | Ref | - | |||||||
| TA | 31 | 146 | 2.86(1.53–5.35) | 0.001 | 0.005 | |||||||
| TT | 3 | 26 | 1.55(0.43–5.66) | 0.5 | 0.5 | |||||||
| ADD | TT/TA/AA | 3/31/17 | 26/146/229 | 1.76(1.13–2.75) | 0.012 | 0.015 | ||||||
| DOM | (TT + TA)/AA | 34/17 | 172/229 | 2.66(1.44–4.92) | 0.0018 | 0.0045 | ||||||
| REC | TT/(TT+TA) | 3/48 | 26/375 | 0.90(0.26–3.09) | 0.87 | 0.015 |
Association between MBP/rs8090438 and AR in KD patients.
Abbreviations: A1/A2, risk/protective allele; Freq_A1/ Freq_A2, minor allele frequency of the SNP in KD-AR patients and KD-NAR patients, respectively; HWE, P value of Hardy–Weinberg equilibrium test; Model, ALLELIC, GENO, CODOM, DOM, ADD, and REC corresponds to the allelic model, 2df genotypic model, co-dominant model, additive model, dominant model and recessive model, respectively; AFF, allele count in KD-AR patients under different models; UNAFF, allele count in KD-NAR patients under different models; OR: odds ratio of the risk allele under different tests; CI, confidence interval; POR and PFDR, unadjusted P values under different tests and multiple testing correction (Bold text indicates POR < 0.05, PFDR < 0.01).
4 Discussion
In accordance with the 2017 AHA Guideline, high-dose aspirin primarily exerts anti-inflammatory effects during the acute phase of KD, whereas low-dose aspirin is principally used for antiplatelet therapy in the subsequent stages (). Accordingly, we systematically examined the clinical correlation between AR and platelet parameters, CBC-derived inflammatory markers, and genetic susceptibility in KD patients.
In this study, AR was defined as a mean aggregation of ≥70% with 10 μM ADP and ≥20% with 0.5 mM AA (). This combined criterion differs from the traditional AA-only assessment, as ADP-induced platelet activation is predominantly mediated via P2Y1 and P2Y12 pathways, but can also promote AA release and secondary COX-1–dependent TXA2 generation, indicating a functional interaction between ADP signaling and the COX-1/TXA2 pathway (). By incorporating both ADP and AA stimuli, this definition may better capture residual platelet reactivity and the clinically observable HPR phenotype, thereby reducing potential false-negative classification associated with single-pathway testing. This criterion has been widely applied in numerous studies to identify HPR high-risk patients. Given the lack of validated pediatric-specific criteria for AR in KD, and the similarity in platelet physiology and the mechanism of action of aspirin across age groups, we adopted these established adult criteria as a reference standard for evaluating AR in our KD cohort.
We identified a significant association between AR and the development of CAA. From a pathophysiological perspective, AR may reflect heightened platelet activation, increased inflammatory burden, or other circulatory disturbances (). In the context of vascular injury, alternative pathways, such as cyclooxygenase-2 (COX-2), may partly contribute to sustained platelet aggregation and thrombotic tendency even when COX-1-mediated TXA2 synthesis is adequately inhibited by aspirin (). Similar mechanisms have been described in cardiovascular research, supporting the view of AR as a risk indicator for coronary artery disease rather than solely a measure of drug responsiveness ().
The longitudinal analysis revealed significant temporal variation in all platelet parameters across the disease course, with distinct phase × group interactions for PLT, PDW, PCT, and PLCR, indicating different dynamic trajectories between the AR and NAR groups. Notably, these differences were predominantly observed during the subacute phase (D7-14), when the AR group showed lower PLT and PCT levels but higher PDW and PLCR. Previous studies have shown that the subacute phase of KD is a critical stage characterized by persistent vascular inflammation and an elevated risk of coronary artery involvement (; ). In this context, the subacute phase has also been associated with active endothelial injury and enhanced platelet activation (; ). The reduced PLT and PCT observed in AR patients may therefore be associated with increased platelet consumption under sustained inflammatory conditions, whereas elevated PDW and PLCR suggest greater platelet size heterogeneity and a higher proportion of larger, more reactive platelets with increased prothrombotic potential (; ). The absence of significant MPV differences suggests that distribution-based parameters may reflect platelet activation status more sensitively than MPV under inflammatory conditions (). Collectively, these findings suggest that AR in KD is associated with phase-specific alterations in platelet dynamics and heterogeneity during the subacute stage, highlighting this period as a clinically relevant window for monitoring platelet-related hematological changes.
SII, PLR and NLR were assessed prior to the initiation of aspirin therapy, thereby minimizing potential confounding effects of treatment on inflammatory status. These CBC-derived inflammatory markers were significantly elevated in KD-AR group, indicating that a heightened inflammatory burden precedes and may underlie impaired aspirin responsiveness. As composite indices integrating neutrophil activation, lymphocyte suppression, and platelet involvement, SII, PLR, and NLR have been widely used to reflect systemic inflammatory status in KD and other cardiovascular inflammatory conditions (; ). In the context of stable sensitivity analyses, RCS analysis demonstrated a statistically significant overall association between SII/PLR/NLR levels and AR risk (Poverall < 0.001), revealing a progressive increase in AR risk observed at higher marker levels. The results collectively demonstrate that CBC-derived inflammatory biomarkers SII/PLR/NLR show early elevation in KD-AR patients, indicating that vigilance should be exercised regarding the risk of AR induced by severe inflammation in KD. Furthermore, ROC curve analyses revealed that SII and NLR demonstrated moderate discriminative performance for distinguishing KD-AR from KD-NAR patients (AUC: 0.702 and 0.722, respectively), whereas PLR showed comparatively lower discriminatory capacity (AUC: 0.626). Although the discriminative ability was modest, these findings suggest that baseline inflammatory indices—particularly NLR and SII—may assist in the early identification of KD patients at higher likelihood of AR. Overall, our results support a close association between enhanced systemic inflammation and AR in KD, highlighting the potential value of routine CBC-derived inflammatory biomarkers as accessible adjunctive indicators for clinical risk stratification.
Beyond clinical parameters, we further explored genetic markers associated with the AR in the KD population. Previous studies have suggested that MBP is involved in immune regulation and may be linked to platelet activation through MAPK1-related pathways (). In conjunction with our DEG and enrichment results, MBP may represent a potential biological link between altered immune transcriptomic profiles and abnormal platelet activation observed in KD-AR. Through integrated bioinformatics and genetic analyses, MBP/rs8090438 was identified as a regulatory genetic variant associated with the AR phenotype in KD. Emerging evidence indicates that variants in immune-related genes may indirectly influence platelet activation and antiplatelet drug responsiveness through inflammation-dependent mechanisms (). Similar gene-drug interaction patterns have been reported in other cardiovascular and inflammatory diseases, highlighting the contribution of host genetic background to individual variability in aspirin responsiveness ().
Limitations of this study should be acknowledged. First, insufficient data on thrombotic events were captured to allow for meaningful statistical analysis in this cohort. Second, although RCS modeling enabled a flexible assessment of exposure-response relationships and ROC analysis demonstrated moderate discriminative performance, the observed AUC values indicated that these biomarkers alone are insufficient for definitive clinical prediction. Therefore, SII, PLR, and NLR should be interpreted as adjunctive screening indicators rather than standalone predictive tools. In addition, no external validation cohort was included, and prospective studies are required to confirm the stability and generalizability of these findings. Finally, the genetic associations identified in this study should be interpreted with caution and warrant replication in larger, independent populations as well as mechanistic investigations to clarify their biological relevance.
5 Conclusion
In conclusion, our research findings suggest that AR in KD is a multi-factorial phenomenon propelled by systemic inflammation, dynamic platelet depletion, and genetic susceptibility, and it is correlated with an elevated risk of CAA. Monitoring inflammatory markers and platelet parameters, especially during the sub-acute phase, may contribute to the early identification of patients at high risk of AR.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
Ethics statement
The study conformed to the Declaration of Helsinki and was performed with the approval of the Ethics Committee of the Women and Children’s Medical Center affiliated with Guangzhou Medical University (approval numbers: 2014073009, 2018052702, and 2021093A01). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.
Author contributions
LJ: Formal Analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing. XD: Formal Analysis, Methodology, Writing – review and editing. WY: Data curation, Formal Analysis, Methodology, Writing – review and editing. XLu: Data curation, Formal Analysis, Writing – review and editing. KC: Formal Analysis, Methodology, Writing – review and editing. LF: Investigation, Methodology, Writing – review and editing. YX: Resources, Writing – review and editing. HZ: Resources, Writing – review and editing. XLi: Resources, Writing – review and editing. CX: Resources, Writing – review and editing. XG: Conceptualization, Writing – review and editing. XY: Writing – review and editing. CW: Writing – review and editing. ZW: Conceptualization, Writing – review and editing. JW: Conceptualization, Writing – review and editing. LP: Conceptualization, Supervision, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study was funded by the 1 Natural Science Foundation of China (grant numbers 82270527 and 82200561) 2 Major Clinical Research Program of Guangzhou Medical University (GMUCR2024-01013), 3 The Guangzhou Science and Technology Program Project (grant numbers 2023A03J0910, 2023A03J0924, SL2023A03J01308, and 2025A03J4388), 4 The Guangzhou Health and Family Planning Science and Technology Project (grant number 20231A011037) 5 The Subject Construction Project of Guangzhou Medical University (grant numbers 02-410-2206062) 6 The STI 2030-Major Projects 2021ZD0200522 7 Science and Technology Program of GuangZhou, Guangzhou Key Laboratory of Pediatric Cardiovascular Disease (grant numbers 2024A03J1165) 8 The Postdoctoral Research Initiation Fund from Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center (grant numbers 2024BS017 and 2024BS019) 9 The Project for Construction of Third-Class Famous Traditional Chinese Medicine Clinics in Guangzhou City for the Year 2024, Xiangna Yang 10 Guangzhou Municipal Health Science and Technology Project (Western Medicine General Orientation Program), 20231A010023.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2026.1807254/full#supplementary-material
References
1
AssempoorR.AbroyA. S.AzarbooA.Ghaseminejad-RaeiniA.NajafiK.HosseiniK. (2024). Different antithrombotic strategies to prevent cardiovascular complications in kawasaki patients: a systematic review and meta-analysis. BMC Pediatr.24 (1), 738. 10.1186/s12887-024-05202-2
2
CattaneoM. (2013). High on-treatment platelet reactivity--definition and measurement. Thromb. Haemost.109 (5), 792–798. 10.1160/TH12-10-0758
3
ChikovaniT.GvetadzeN.AbashishviliL.ShalamberidzeL.KikodzeN. (2023). Relationship of complete blood count derived biomarkers with methotrexate resistance. Cureus15 (12), e50765. 10.7759/cureus.50765
4
DallaireF.Fortier-MorissetteZ.BlaisS.DhanrajaniA.BasodanD.RenaudC.et al (2017). Aspirin dose and prevention of coronary abnormalities in Kawasaki disease. Pediatrics139 (6), e20170098. 10.1542/peds.2017-0098
5
DandonaS. (2014). Cardiovascular drugs and the genetic response. Methodist Debakey Cardiovasc J.10 (1), 13–17. 10.14797/mdcj-10-1-13
6
DavìG.PatronoC. (2007). Platelet activation and atherothrombosis. N. Engl. J. Med.357 (24), 2482–2494. 10.1056/NEJMra071014
7
FreynhoferM. K.GruberS. C.GroveE. L.WeissT. W.WojtaJ.HuberK. (2015). Antiplatelet drugs in patients with enhanced platelet turnover: biomarkers versus platelet function testing. Thromb. Haemost.114 (3), 459–468. 10.1160/TH15-02-0179
8
FullerM. G. (2019). Kawasaki disease in infancy. Adv. Emerg. Nurs. J.41 (3), 222–228. 10.1097/TME.0000000000000253
9
GasparyanA. Y.AyvazyanL.MikhailidisD. P.KitasG. D. (2011). Mean platelet volume: a link between thrombosis and inflammation?Curr. Pharm. Des.17 (1), 47–58. 10.2174/138161211795049804
10
GoodmanT.FerroA.SharmaP. (2008). Pharmacogenetics of aspirin resistance: a comprehensive systematic review. Br. J. Clin. Pharmacol.66 (2), 222–232. 10.1111/j.1365-2125.2008.03183.x
11
GordonJ. B.KahnA. M.BurnsJ. C. (2009). When children with Kawasaki disease grow up: myocardial and vascular complications in adulthood. J. Am. Coll. Cardiol.54 (21), 1911–1920. 10.1016/j.jacc.2009.04.102
12
GumP. A.Kottke-MarchantK.PoggioE. D.GurmH.WelshP. A.BrooksL.et al (2001). Profile and prevalence of aspirin resistance in patients with cardiovascular disease. Am. J. Cardiol.88 (3), 230–235. 10.1016/s0002-9149(01)01631-9
13
GumP. A.Kottke-MarchantK.WelshP. A.WhiteJ.TopolE. J. (2003). A prospective, blinded determination of the natural history of aspirin resistance among stable patients with cardiovascular disease. J. Am. Coll. Cardiol.41 (6), 961–965. 10.1016/s0735-1097(02)03014-0
14
HaiyanG.JianmingL.SuqianT.DongQ.ShuangL.JinZ. (2022). Blood routine risk factors for coronary artery aneurysm in infants younger than 8 months with Kawasaki disease. BMC Pediatr.22 (1), 29. 10.1186/s12887-021-03083-3
15
HanjisC.FrishmanW. H.LernerR. G. (2006). Aspirin resistance: mechanisms and clinical implications. Cardiol. Rev.14 (1), 18–25. 10.1097/01.crd.0000148175.60718.69
16
IkonnikovaA.AnisimovaA.GalkinS.GunchenkoA.AbdukhalikovaZ.FilippovaM.et al (2022). Genetic association study and machine learning to investigate differences in platelet reactivity in patients with acute ischemic stroke treated with aspirin. Biomedicines10 (10), 2564. 10.3390/biomedicines10102564
17
JinJ.QuintonT. M.ZhangJ.RittenhouseS. E.KunapuliS. P. (2002). Adenosine diphosphate (ADP)-Induced thromboxane A(2) generation in human platelets requires coordinated signaling through integrin alpha(IIb)beta(3) and ADP receptors. Blood99 (1), 193–198. 10.1182/blood.v99.1.193
18
LiD.ChenX.LiX.YuanY.JinH.LiuG.et al (2021). Effectiveness and safety of dual antiplatelet therapy in coronary aneurysms caused by Kawasaki disease in children: study protocol for a multicenter randomized clinical trial. Transl. Pediatr.10 (7), 1914–1923. 10.21037/tp-21-74
19
LiY.ZhouX.WangX.DongS.ZhangZ.JinZ.et al (2025). Highly specific screening of aspirin resistance-related single-nucleotide polymorphisms using ligase chain reaction strategy. Talanta282, 126939. 10.1016/j.talanta.2024.126939
20
LigiD.Della FrancaC.NotarteK. I.GoldrichN.KavteladzeD.HenryB. M.et al (2023). Platelet distribution width (PDW) as a significant correlate of COVID-19 infection severity and mortality. Clin. Chem. Lab. Med.62 (3), 385–395. 10.1515/cclm-2023-0625
21
LimY. T.KwonJ. E.KimY. H. (2025). Clinical utility of CBC-derived inflammatory markers for predicting IVIG resistance: findings from a Korean infant Kawasaki disease cohort. Pediatr. Rheumatol. Online J.23 (1), 116. 10.1186/s12969-025-01162-8
22
LiuC.WuJ. (2022). Value of blood inflammatory markers for predicting intravenous immunoglobulin resistance in Kawasaki disease: a systematic review and meta-analysis. Front. Pediatr.10, 969502. 10.3389/fped.2022.969502
23
LiuQ.GuoS.WangN.WangK.MoS.LiX.et al (2024). Model based on single-nucleotide polymorphism to discriminate aspirin resistance patients. Stroke Vasc. Neurol.9 (3), 212–220. 10.1136/svn-2022-002228
24
López-EscobarA.MadurgaR.CastellanoJ. M.Ruiz de AguiarS.VelázquezS.BucarM.et al (2021). Hemogram as marker of in-hospital mortality in COVID-19. J. Investig. Med.69 (5), 962–969. 10.1136/jim-2021-001810
25
LordkipanidzéM.SoD.TanguayJ. F. (2016). Platelet function testing as a biomarker for efficacy of antiplatelet drugs. Biomark. Med.10 (8), 903–918. 10.2217/bmm-2016-0070
26
McCrindleB. W.RowleyA. H.NewburgerJ. W.BurnsJ. C.BolgerA. F.GewitzM.et al (2017). Diagnosis, treatment, and long-term management of Kawasaki disease: a scientific statement for health professionals from the American heart association. Circulation135 (17), e927–e999. 10.1161/CIR.0000000000000484
27
MengL.LiX. H.LiuJ. J.ZhangM. M.LiA. J.ShiL. (2019). Effect of aspirin on anti-platelet aggregation function in children with Kawasaki disease. Chin. J. Pract. Pediatr.34 (8), 670–674. 10.19538/j.ek2019080613
28
MiuraM.KobayashiT.KanekoT.AyusawaM.FukazawaR.FukushimaN.et al (2018). Association of severity of coronary artery aneurysms in patients with Kawasaki disease and risk of later coronary events. JAMA Pediatr.172, e180030. 10.1001/jamapediatrics.2018.0030
29
MonteiroJ. J. G. M.de Oliveira Cipriano TorresD.FilhoD. C. S. (2019). Hematological parameters as prognostic biomarkers in patients with cardiovascular diseases. Curr. Cardiol. Rev.15 (4), 274–282. 10.2174/1573403X15666190225123544
30
PasalicL.WangS. S. Y.ChenV. M. Y. (2016). Platelets as biomarkers of coronary artery disease. Semin. Thromb. Hemost.42 (3), 223–233. 10.1055/s-0036-1572328
31
PatronoC.García RodríguezL. A.LandolfiR.BaigentC. (2005). Low-dose aspirin for the prevention of atherothrombosis. N. Engl. J. Med.353 (22), 2373–2383. 10.1056/NEJMra052717
32
PedersenO. B.NissenP. H.PasalicL.HvasA. M.KristensenS. D.GroveE. L. (2024). Changes in platelet maturity and reactivity following acute ST-segment elevation myocardial infarction. Res. Pract. Thromb. Haemost.9 (1), 102652. 10.1016/j.rpth.2024.102652
33
RifeE.GedaliaA. (2020). Kawasaki disease: an update. Curr. Rheumatol. Rep.22 (10), 75. 10.1007/s11926-020-00941-4
34
RowleyA. H. (2011). Kawasaki disease: novel insights into etiology and genetic susceptibility. Annu. Rev. Med.62, 69–77. 10.1146/annurev-med-042409-151944
35
SamieiM.SangheraJ. S.PelechS. L. (1993). Activation of myelin basic protein and S6 peptide kinases in phorbol ester- and PAF-treated sheep platelets. Biochim. Biophys. Acta1176 (3), 287–298. 10.1016/0167-4889(93)90057-v
36
ShulmanS. T.RowleyA. H. (2015). Kawasaki disease: insights into pathogenesis and approaches to treatment. Nat. Rev. Rheumatol.11, 475–482. 10.1038/nrrheum.2015.54
37
VinholtP. J.HvasA. M.NyboM. (2014). An overview of platelet indices and methods for evaluating platelet function in thrombocytopenic patients. Eur. J. Haematol.92 (5), 367–376. 10.1111/ejh.12262
38
VinholtP. J.FrederiksenH.HvasA. M.SprogøeU.NielsenC. (2017). Measurement of platelet aggregation, independently of patient platelet count: a flow-cytometric approach. J. Thromb. Haemost.15 (6), 1191–1202. 10.1111/jth.13675
39
WangF.LuZ.LvH. (2025). Age-specific cardiovascular and platelet dynamics in Kawasaki disease. Int. J. Gen. Med.18, 6265–6278. 10.2147/IJGM.S515118
40
YiC.ZhouY. N.GuoJ.ChenJ.SheX. (2024). Novel predictors of intravenous immunoglobulin resistance in patients with Kawasaki disease: a retrospective study. Front. Immunol.15, 1399150. 10.3389/fimmu.2024.1399150
41
YontarG.MutluE. A. (2024). Neutrophil-to-lymphocyte, platelet-to-lymphocyte ratios and systemic immune-inflammation index in patients with post-traumatic stress disorder. BMC Psychiatry24 (1), 966. 10.1186/s12888-024-06439-y
42
YuX.WuD.SongG. (2022). Assessment of endothelial dysfunction in patients with Kawasaki disease: a meta-analysis. Rev. Cardiovasc Med.23 (8), 260. 10.31083/j.rcm2308260
43
ZengW.ChuT. T.ChowE. Y.HuM.FokB. S.ChanJ. C.et al (2024). Genetic factors related to aspirin resistance using the multiplate® device in Hong Kong Chinese patients with stable coronary heart disease. Heliyon10 (14), e34552. 10.1016/j.heliyon.2024.e34552
44
ZhaoY.YangS.WuM. (2021). Mechanism of improving aspirin resistance: blood-activating herbs combined with aspirin in treating atherosclerotic cardiovascular diseases. Front. Pharmacol.12, 794417. 10.3389/fphar.2021.794417
45
ZhouE.WuJ.ZhouX.YinY. (2024). Systemic inflammatory biomarkers are novel predictors of all-cause and cardiovascular mortality in individuals with osteoarthritis: a prospective cohort study using data from the NHANES. BMC Public Health24 (1), 1586. 10.1186/s12889-024-19105-5
Summary
Keywords
aspirin resistance, genetic susceptibility, inflammatory indices, kawasaki disease, platelet parameters
Citation
Jiang L, Ding X, Yang W, Luo X, Chen K, Fu L, Xu Y, Zhou H, Li X, Xiao C, Gu X, Yang X, Wei C, Wang Z, Wei J and Pi L (2026) Platelet-related hematologic markers and genetic associations of aspirin resistance in kawasaki disease. Front. Mol. Biosci. 13:1807254. doi: 10.3389/fmolb.2026.1807254
Received
09 February 2026
Revised
09 March 2026
Accepted
12 March 2026
Published
01 April 2026
Volume
13 - 2026
Edited by
Peng Zhang, Institute of ENT and Shenzhen Key Laboratory of ENT, China
Updates
Copyright
© 2026 Jiang, Ding, Yang, Luo, Chen, Fu, Xu, Zhou, Li, Xiao, Gu, Yang, Wei, Wang, Wei and Pi.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhouping Wang, wang_zhouping@gwcmc.com; Jianrui Wei, jianruiw@163.com; Lei Pi, pilei@gwcmc.org
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.