Socioeconomic and regional influences on eosinophilic inflammation in chronic rhinosinusitis with nasal polyps

Hao-Sheng Ni; Li-Yuan Zhang; Han-Qiang Lu; Zhi-Cun Zhang; Min Zhang; Mei-Ping Lu; Lei Cheng

doi:10.1016/j.waojou.2026.101440

. 2026 Jul 30;19(8):101440. doi: 10.1016/j.waojou.2026.101440

Socioeconomic and regional influences on eosinophilic inflammation in chronic rhinosinusitis with nasal polyps

Hao-Sheng Ni ^a,^f, Li-Yuan Zhang ^b,^c,^f, Han-Qiang Lu ^d,^f, Zhi-Cun Zhang ^e,^f, Min Zhang ^b, Mei-Ping Lu ^b, Lei Cheng ^b,^c,^⁎

PMCID: PMC13451809 PMID: 42571263

Abstract

Background

Eosinophilic inflammation is a key feature of chronic rhinosinusitis with nasal polyps (CRSwNP); however, the extent to which peripheral blood eosinophil counts reflect local nasal eosinophilia remains controversial, particularly across different geographic regions and socioeconomic contexts.

Methods

We conducted a multicenter retrospective study involving 1047 patients with CRSwNP who underwent endoscopic sinus surgery at 3 tertiary hospitals in Jiangsu Province, China. Peripheral blood eosinophil counts and nasal mucosa eosinophil counts from polyp tissue were measured at 3 time points (2008, 2012, and 2016). Correlation analyses, receiver operating characteristic (ROC) curve analysis, linear regression models, and linear mixed-effects models were used to evaluate the association between blood and tissue eosinophil counts, as well as their temporal and regional variability. City-level gross domestic product (GDP) and annual mean PM2.5 concentrations were incorporated to explore potential socioeconomic and environmental associations.

Results

Peripheral blood eosinophil counts showed a weak correlation with nasal mucosa eosinophil counts (ρ = 0.28) and had limited predictive value for CRSwNP (AUC = 0.607). Eosinophil levels varied significantly across hospitals and time points, with no consistent temporal trends observed. Linear regression analysis revealed a modest overall association between peripheral blood and nasal mucosa eosinophil counts (adjusted R² = 0.053). Linear mixed-effects models confirmed heterogeneous temporal trends in both blood and tissue eosinophil counts across hospitals (P < 0.01). Additionally, city-level analyses revealed positive correlations between mean peripheral blood eosinophil counts and total GDP (ρ = 0.80, P = 0.014) and between nasal mucosa eosinophil counts and GDP per capita (ρ = 0.72, P = 0.037). In contrast, annual mean PM2.5 concentration was not significantly associated with either peripheral blood or nasal mucosa eosinophil counts.

Conclusion

Peripheral blood eosinophil count is a poor surrogate for local tissue inflammation in CRSwNP. Substantial regional, temporal, and socioeconomic heterogeneity underscores the need to consider geographic context when interpreting eosinophil-based biomarkers in clinical assessment and disease stratification.

Keywords: Chronic rhinosinusitis with nasal polyps, Eosinophilic inflammation, Peripheral blood eosinophils, Regional variability, Socioeconomic factors, Tissue eosinophils

Introduction

Chronic rhinosinusitis (CRS) is a prevalent inflammatory disease of the nasal and paranasal mucosa and is commonly classified into chronic rhinosinusitis with nasal polyps (CRSwNP) and without nasal polyps.¹^,² Among these, CRSwNP represents a particularly challenging subtype, characterized by recurrent symptoms, frequent need for systemic corticosteroids, and a high rate of surgical recurrence.³^,⁴ Notably, approximately 20% of patients undergoing primary endoscopic sinus surgery experience persistent symptoms and eventually require revision surgeries.⁴ These challenges underscore the need for a better understanding of the inflammatory mechanisms driving CRSwNP.⁴^,⁵ Identifying distinct phenotypes and endotypes of CRS has therefore become crucial for guiding therapeutic decisions.⁶ However, CRS inflammation is highly heterogeneous, and the prevalence of various endotypes differs significantly across geographic regions.⁷^,⁸

As bone marrow-derived granulocytes, eosinophils are involved in various physiological and pathological processes, including immune regulation, inflammatory amplification, tissue remodeling, and regulation of the tumor microenvironment.9, 10, 11, 12 In the context of CRSwNP, eosinophilic inflammation is widely recognized as a central pathological mechanism.¹³ Eosinophils release various vasoactive and cytotoxic substances, including leukotrienes, eosinophilic cationic protein, major basic protein, platelet-activating factor, and eosinophilic peroxidase.9, 10, 11, 12 These factors amplify the inflammatory response, increase the expression of platelet tissue factor, and trigger the coagulation cascade, leading to excessive fibrin deposition in the nasal mucosa and the development of nasal polyps.¹⁴^,¹⁵ Additionally, eosinophils alter mucus properties in CRS by forming aggregates and releasing specific proteins, further exacerbating the disease.¹⁶

Given their pathogenic relevance and prognostic value, CRS is often classified into eosinophilic CRS (ECRS) and non-eosinophilic CRS (NECRS) based on tissue eosinophil infiltration.¹⁷^,¹⁸ Compared with NECRS, ECRS is more frequently associated with asthma, aspirin intolerance, severe symptoms, higher endoscopic and radiologic scores, and increased recurrence risk.¹⁹ While eosinophilic inflammation predominates in Western CRSwNP populations, Asian cohorts, including those from China, have historically shown lower eosinophil burden and greater inflammatory heterogeneity.²⁰ However, recent reports indicate an increasing prevalence of eosinophilic CRSwNP patients in Beijing, China.²¹ Concordant eosinophilia in blood and tissue has been associated with worse disease control in CRS,²² but the extent to which peripheral blood eosinophil counts reliably reflect local sinonasal eosinophilic inflammation remains controversial.²³ In parallel, previous studies have reported that socioeconomic factors, including income level, education, and access to healthcare, influence the onset, progression, and outcomes of various diseases,24, 25, 26 further complicating the interpretation of eosinophilic phenotypes across populations and over time. In addition, environmental exposures, including air pollution, may vary across regions and over time and may further contribute to the heterogeneity of airway inflammatory profiles.²⁷ Data addressing these issues in Asian CRSwNP cohorts remain limited.

Therefore, in this retrospective study, we aimed to investigate the eosinophil counts in both peripheral blood and nasal mucosa of CRSwNP patients from 3 cities in Jiangsu Province, China, over 3 different time points (2008, 2012, and 2016). By examining temporal trends, regional variation, and the relationship between blood and tissue eosinophilia, we aimed to clarify the heterogeneity of eosinophilic inflammation in CRSwNP and its potential implications for clinical assessment. In addition, we conducted exploratory city-level analyses to assess whether socioeconomic and environmental indicators—such as gross domestic product (GDP) and annual mean fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5)—were associated with eosinophilic inflammation.

Methods

Study design and subjects

This multicenter retrospective study was conducted at 3 tertiary hospitals in Jiangsu Province, East China: the Affiliated Hospital of Jiangsu University (AHJU) in Zhenjiang, the Affiliated Hospital of Nantong University (AHNU) in Nantong, and Huai'an First People's Hospital (HFPH, also known as the Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University) in Huai'an. Data were collected from patients who underwent endoscopic sinus surgery for CRSwNP at 3 distinct time points: 2008, 2012, and 2016. The inclusion criteria were as follows: 1) Diagnosis of CRSwNP based on the Chinese Guidelines for the Diagnosis and Treatment of Chronic Rhinosinusitis (2012, Kunming);²⁸ 2) Persistent symptoms despite at least 12 weeks of standard medical therapy; 3) No prior history of surgical intervention. The exclusion criteria included: 1) Use of aspirin or other anticoagulants within 1 week before surgery; 2) Acute upper respiratory tract infection within the preceding month; 3) Severe cardiac or pulmonary diseases that presented surgical risks; 4) Autoimmune disorders, parasitic infections, or rheumatic diseases; 5) Presence of posterior nostril polyps or hemorrhagic/necrotic polyps.

Patient demographic and clinical data were retrieved from electronic medical records, including age, sex, laboratory results, and imaging findings. Ethical approval was granted by the ethics committees of the 3 participating hospitals (Approval IDs: 2023-K136-01, KY-2023-179-01, and KY2023K1104).

Eosinophil counting

Peripheral blood eosinophil counts were derived from routine blood tests performed 1 day prior to surgery. To assess local eosinophilic inflammation of the nasal mucosa, nasal polyp tissue was obtained from the middle meatus during endoscopic sinus surgery, with approximate dimensions of 5 mm × 3 mm, and immediately fixed in 4% paraformaldehyde. A graded ethanol dehydration protocol was employed: 50%–90% ethanol for 1 hour each, followed by 95%–100% ethanol for 2 30-min stages. To ensure optimal tissue processing, tissues were left overnight in 95% ethanol before being cleared and embedded in paraffin wax. Sections (5 μm) were stained with H&E and reviewed at 400 × magnification (objective, 40 × ). The high-power field (HPF) area was 0.24 mm² on an Olympus BX43 microscope (field number: 5). Two board-certified pathologists independently counted eosinophils in 5 non-overlapping HPFs per case, blinded to site, year, and blood counts; discrepancies were resolved by consensus.

City-level GDP and PM2.5 data sources

City-level GDP data for the 3 cities (Zhenjiang, Nantong, and Huai'an) were obtained from official government reports for 2008, 2012, and 2016. Annual mean PM2.5 concentrations for the corresponding cities and years were extracted from a published city-scale PM2.5 dataset derived from satellite observations, chemical transport modeling, and ground-based monitoring data.²⁹^,³⁰ City-level annual mean concentrations were calculated from PM2.5 raster data within each municipality's administrative boundaries. GDP and PM2.5 data were matched to the corresponding city-year mean eosinophil counts for exploratory city-level analyses.

Statistical analysis

Data analyses were conducted using R software (version 4.2.2). Continuous variables are expressed as means ± standard deviations (M ± SD), and categorical variables are presented as counts (percentages). The Shapiro-Wilk test was used to determine whether continuous variables followed a normal distribution. For normally distributed variables, t-tests or ANOVA were used for group comparisons. For categorical variables, the chi-square test or Fisher's exact test was applied. For non-normally distributed continuous variables, Mann-Whitney U test was used for two-group comparisons and Kruskal-Wallis test for multi-group comparisons. To examine the relationship between peripheral blood eosinophil counts and nasal mucosa eosinophil counts, Spearman's correlation analysis was used. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive value of peripheral blood eosinophil counts for nasal mucosa eosinophilia, with area under the curve (AUC) values calculated. For ROC analyses, nasal mucosa eosinophilia was defined a priori using the cohort mean of nasal polyp tissue eosinophil counts (56.80 cells/HPF) as the threshold. Values above this threshold were coded as eosinophilic (1), and values at or below this threshold were coded as non-eosinophilic (0). Linear regression models were applied to investigate the association between peripheral blood eosinophil counts and nasal mucosa eosinophil levels. In stratified analyses by hospital and year, age and sex were included as covariates. In the combined analysis, age, sex, hospital, and year were included as covariates. Additionally, Spearman's correlation analyses were used to examine the associations between city-year mean eosinophil counts and city-level GDP or annual mean PM2.5 concentration. Moreover, linear mixed-effects models were fitted to evaluate whether temporal trajectories of tissue and blood eosinophil counts varied across hospitals. Models allowing hospital-specific year slopes were compared with random-intercept models using likelihood-ratio tests. A significance level of P < 0.05 was considered statistically significant.

Results

Demographic characteristics

This study included a total of 1047 patients diagnosed with CRSwNP. All patients underwent endoscopic surgery between January and December of 2008, 2012, and 2016 (Table 1). Among the study population, 361 were female (34.5%) and 686 were male (65.5%), with an age range of 6–87 years old (mean age: 44.71 years). The mean peripheral blood eosinophil counts were 0.14 ± 0.17 × 10⁹/L (2008), 0.15 ± 0.22 × 10⁹/L (2012), and 0.20 ± 0.20 × 10⁹/L (2016), with an overall mean of 0.16 ± 0.20 × 10⁹/L. The mean nasal mucosa eosinophil counts were 52.46 ± 62.67 (2008), 54.73 ± 80.61 (2012), and 64.88 ± 78.61 (2016), with an overall mean of 56.80 ± 74.31.

Table 1.

Baseline characteristics of the study population.

Variables	Total (n = 1047)	Year			P value
Variables	Total (n = 1047)	2008 (n = 367)	2012 (n = 384)	2016 (n = 296)	P value
Age, n (%)					0.555^a
≥ 18 years old	970 (92.6)	344 (93.7)	355 (92.4)	271 (91.6)
< 18 years old	77 (7.4)	23 (6.3)	29 (7.6)	25 (8.4)
Sex, n (%)					0.385^a
Female	361 (34.5)	136 (37.1)	124 (32.3)	101 (34.1)
Male	686 (65.5)	231 (62.9)	260 (67.7)	195 (65.9)
Hospital, n (%)					0.074^a
AHJU	410 (39.2)	145 (39.5)	145 (37.8)	120 (40.5)
AHNU	303 (28.9)	122 (33.2)	108 (28.1)	73 (24.7)
HFPH	334 (31.9)	100 (27.2)	131 (34.1)	103 (34.8)

Open in a new tab

Abbreviations: AHJU, Affiliated Hospital of Jiangsu University; AHNU, Affiliated Hospital of Nantong University; HFPH, Huai'an First People's Hospital.

χ² test.

Regional variations in peripheral blood and nasal mucosa eosinophil counts

Within each study year, peripheral blood eosinophil counts varied significantly among the 3 tertiary hospitals (Table 2, Fig. 1A–C), indicating regional variability in systemic eosinophilic inflammation. Notably, peripheral blood eosinophil counts at HFPH were generally lower than those at the other 2 hospitals. In 2008, the counts at AHJU, AHNU, and HFPH were 0.18 ± 0.19 × 10⁹/L, 0.19 ± 0.18 × 10⁹/L, and 0.03 ± 0.04 × 10⁹/L, respectively. In 2012, the corresponding counts were 0.18 ± 0.26 × 10⁹/L, 0.26 ± 0.20 × 10⁹/L, and 0.02 ± 0.04 × 10⁹/L, respectively. In 2016, the counts were 0.20 ± 0.22 × 10⁹/L, 0.25 ± 0.23 × 10⁹/L, and 0.15 ± 0.12 × 10⁹/L, respectively.

Table 2.

Peripheral blood eosinophil counts and nasal mucosa eosinophil counts across different years and hospitals.

Eosinophil counts	Hospital	Counts	Total	Year			P value
Eosinophil counts	Hospital	Counts	Total	2008	2012	2016	P value
Peripheral blood eosinophils ( × 10⁹/L)	AHJU	M (SD)	0.186 (0.224)	0.175 (0.188)	0.182 (0.256)	0.203 (0.222)	0.335^a
		Median	0.100	0.100	0.100	0.100
	AHNU	M (SD)	0.231 (0.201)	0.194 (0.179)	0.259 (0.202)	0.252 (0.225)	0.009^a
		Median	0.150	0.135	0.195	0.150
	HFPH	M (SD)	0.060 (0.094)	0.025 (0.039)	0.017 (0.036)	0.148 (0.120)	< 0.001^a
		Median	0.017	0.014	0.008	0.105
	Total	M (SD)	0.159 (0.197)	0.140 (0.173)	0.147 (0.215)	0.196 (0.197)	< 0.001^a
		Median	0.100	0.100	0.100	0.124
	P value		< 0.001^b	< 0.001^b	< 0.001^b	0.007^b
Nasal mucosa eosinophils (cells/HPF)	AHJU	M (SD)	71.35 (101.88)	59.48 (88.04)	66.42 (113.57)	91.63 (100.36)	< 0.001^a
		Median	27.00	19.00	23.00	43.00
	AHNU	M (SD)	53.54 (16.99)	47.82 (13.14)	54.27 (16.97)	62.01 (19.07)	< 0.001^a
		Median	53.00	46.50	48.00	65.00
	HFPH	M (SD)	41.90 (62.01)	47.92 (53.98)	42.16 (65.63)	35.73 (64.50)	0.005^a
		Median	15.40	27.00	15.80	7.60
	Total	M (SD)	56.80 (74.31)	52.46 (62.67)	54.73 (80.61)	64.88 (78.61)	0.037^a
		Median	38.50	41.00	36.00	43.00
	P value		< 0.001^b	< 0.001^b	< 0.001^b	< 0.001^b

Open in a new tab

Abbreviations: AHJU, Affiliated Hospital of Jiangsu University; AHNU, Affiliated Hospital of Nantong University; HFPH, Huai'an First People's Hospital; HPF, high-power field; M (SD), mean (standard deviation).

Kruskal–Wallis test for eosinophil counts across the years 2008, 2012, and 2016.

Kruskal–Wallis test for eosinophil counts among the hospitals AHJU, AHNU, and HFPH.

Similarly, nasal mucosa eosinophil counts also differed significantly among hospitals within the same year (Table 2, Fig. 1D–F). In 2008, the counts at AHJU, AHNU, and HFPH were 59.48 ± 88.04, 47.82 ± 13.14, and 47.92 ± 53.98 cells/HPF, respectively. In 2012, the corresponding counts were 66.42 ± 113.57, 54.27 ± 16.97, and 42.16 ± 65.63 cells/HPF, respectively. In 2016, the counts were 91.63 ± 100.36, 62.01 ± 19.07, and 35.73 ± 64.50 cells/HPF, respectively.

Temporal trends in peripheral blood and nasal mucosa eosinophil counts

Temporal trends in peripheral blood eosinophil counts were further analyzed within each hospital (2008, 2012, and 2016) (Table 2, Fig. 2A–C). Significant differences over time were observed at HFPH (P < 0.001) and AHNU (P = 0.009). At HFPH, eosinophil counts were 0.03 ± 0.04 × 10⁹/L in 2008, 0.02 ± 0.04 × 10⁹/L in 2012, and 0.15 ± 0.12 × 10⁹/L in 2016, with a marked increase in 2016. At AHNU, eosinophil counts were 0.19 ± 0.18 × 10⁹/L in 2008, 0.26 ± 0.20 × 10⁹/L in 2012, and 0.25 ± 0.23 × 10⁹/L in 2016, with significantly lower counts observed in 2008. However, at AHJU, eosinophil counts were 0.18 ± 0.19 × 10⁹/L in 2008, 0.18 ± 0.26 × 10⁹/L in 2012, and 0.20 ± 0.22 × 10⁹/L in 2016, showing no significant variation (P = 0.335).

Nasal mucosa eosinophil counts also changed over time within each hospital (Table 2, Fig. 2D–F). At AHJU and AHNU, the nasal mucosa eosinophil counts were highest in 2016, whereas at HFPH, the highest counts were observed in 2008. Although variations in eosinophil counts were noted across years at all hospitals, no consistent temporal trend was observed. Specifically, at AHJU, the nasal mucosa eosinophil counts were 59.48 ± 88.04 cells/HPF in 2008, 66.42 ± 113.57 cells/HPF in 2012, and 91.63 ± 100.36 cells/HPF in 2016. At HFPH, the counts declined from 47.92 ± 53.98 cells/HPF in 2008 to 42.16 ± 65.63 cells/HPF in 2012 and 35.73 ± 64.50 cells/HPF in 2016. At AHNU, the counts gradually increased: 47.82 ± 13.14 cells/HPF in 2008, 54.27 ± 16.97 cells/HPF in 2012, and 62.01 ± 19.07 cells/HPF in 2016.

Sex- and age-based comparisons

No significant differences in either peripheral blood or nasal mucosa eosinophil counts were found between female and male patients (eTable 1). However, significant differences in both peripheral blood and nasal mucosa eosinophil counts were observed between age groups. When patients were classified into 2 age groups (<18 years and ≥18 years) based on a previously published report,¹¹ those aged ≥18 years showed higher peripheral blood and nasal mucosa eosinophil levels (eTable 1).

Correlation analysis

Spearman's correlation analysis showed a modest correlation between nasal mucosa eosinophil counts and peripheral blood eosinophil counts, though the strength of the correlation varied depending on the dataset. Both the AHJU and HFPH datasets exhibited weak positive correlations, with ρ = 0.23 (P = 3.75 × 10⁻⁶) and ρ = 0.21 (P = 1.50 × 10⁻⁴), respectively (Fig. 3A and Fig. 3C). In contrast, the AHNU dataset showed no significant correlation (ρ = 0.06, P = 0.27) (Fig. 3B). When all datasets were combined and analyzed, a weak positive correlation was detected (ρ = 0.28, P = 1.22 × 10⁻¹⁹) (Fig. 3D). These results suggest a potential, albeit weak, biological association between nasal mucosa and peripheral blood eosinophil counts.

Fig. 3 — **Correlations between peripheral blood and nasal mucosa eosinophil counts**. (A) AHJU. (B) AHNU. (C) HFPH. (D) Combined dataset. Regression lines with 95% confidence intervals are shown. Spearman correlation coefficients (ρ) and P values are reported. *Abbreviations:* AHJU, Affiliated Hospital of Jiangsu University; AHNU, Affiliated Hospital of Nantong University; HFPH, Huai'an First People's Hospital.

ROC curve analysis

To further evaluate the predictive ability of peripheral blood eosinophil counts for nasal mucosa eosinophil counts, ROC curve analysis was performed. The data were stratified by hospital (AHJU, AHNU, and HFPH) and year (2008, 2012, and 2016) (eFig. 1A–I), as well as in the full datasets (eFig. 1J). The AUC values across different hospitals and years ranged from 0.473 to 0.777, with the highest AUC observed in HFPH-2016 (AUC = 0.777) (eFig. 1I) and the lowest in AHNU-2016 (AUC = 0.473) (eFig. 1F). When all data were pooled, the AUC was approximately 0.607 (eFig. 1J), indicating limited predictive capacity.

Linear regression analysis

Linear regression analyses were performed to evaluate the association between peripheral blood eosinophil counts and nasal mucosa eosinophil counts, stratified by hospital (AHJU, AHNU, and HFPH) and year (2008, 2012, and 2016) (eFig. 2A–I), as well as in the combined dataset (eFig. 2J).

Most subgroups showed positive slopes, indicating a weak positive relationship between blood and tissue counts. Notably, at HFPH in 2012 and 2016, the regression coefficients were relatively high (slope = 795.305, R² = 0.19; slope = 276.784, R² = 0.265) and highly significant (P < 0.001), suggesting a stronger association in these specific years (eFig. 2H and I). However, at AHNU, the slopes were small or even negative, with weak correlations (slope = −3.171, R² = 0.001, P = 0.753). When all datasets were combined (eFig. 2J), the overall linear relationship remained significant (P = 1.1 × 10⁻⁸). However, with R² = 0.03, the results indicated that blood counts could explain only a small portion of the variation in tissue counts.

For further analyses, linear regression analyses were conducted when adjusting sex and age as covariates for each hospital and year group (Fig. 4A–I). For the combined dataset (Fig. 4J), sex, age, hospital, and time were adjusted as covariates. The results were consistent with the analyses without covariates. In the combined dataset (Fig. 4J), the regression model produced a slope of 56.128, with P = 5.58 × 10⁻⁶ and R² = 0.053. While the findings were statistically significant, the low R² value suggested that blood eosinophil counts could explain only a small fraction of the variability in tissue eosinophil counts.

Moreover, linear mixed-effects models were fitted to evaluate whether temporal trajectories of tissue and blood eosinophil counts varied across hospitals. Nasal mucosa eosinophil counts were modelled with hospital as a random effect. Allowing hospital-specific temporal trajectories significantly improved model fit (likelihood-ratio test for a random year slope: χ² (2) = 28.924, P = 5.24 × 10⁻⁷), confirming heterogeneous trends across hospitals. A similar analysis for peripheral blood eosinophils also demonstrated significant heterogeneity (χ² (2) = 9.307, P = 9.53 × 10⁻³). These results indicate that changes over time were not uniform across centers, which aligns with our descriptive findings.

Association with GDP

We further examined the relationship between eosinophil counts and economic status. GDP data for the 3 cities (Zhenjiang, Nantong, and Huai'an) in 2008, 2012, and 2016 were retrieved (Table 3), and Spearman's correlation analysis was performed. Notably, the results revealed a positive correlation between the mean peripheral blood eosinophil count and local GDP (ρ = 0.8, P = 0.014), as well as a positive correlation between the mean nasal mucosa eosinophil count and local GDP per capita (ρ = 0.72, P = 0.037) (Fig. 5). The correlations were based on aggregated city-level mean eosinophil counts from 3 centers across 3 time points (n = 9 city-year data pairs). Moreover, we have performed additional analyses to evaluate whether the associations between GDP and eosinophil counts were consistent when considering the 3 cities as a whole and when analyzing each city separately (eTable 2). Our results suggested a potential variability in the GDP-eosinophil relationship across centers but require cautious interpretation.

Table 3.

Mean peripheral blood and tissue eosinophil counts and local GDP.

Year	Hospital	City ^a	GDP (billion Yuan)	GDP per capita (Yuan)	Mean peripheral blood eosinophils ( × 10⁹/L)	Mean tissue eosinophils (cells/HPF)
2008	AHJU	City A	140.814	46473	0.175	59.48
	AHNU	City B	251.013	35040	0.194	47.82
	HFPH	City C	91.583	18900	0.025	47.92
2012	AHJU	City A	263.01	83636	0.182	66.42
	AHNU	City B	455.87	62506	0.259	54.27
	HFPH	City C	192.091	39992	0.017	42.16
2016	AHJU	City A	383.384	120603	0.203	91.63
	AHNU	City B	676.82	92702	0.252	62.01
	HFPH	City C	304.8	62446	0.148	35.73

Open in a new tab

Abbreviations: AHJU, Affiliated Hospital of Jiangsu University; AHNU, Affiliated Hospital of Nantong University; HFPH, Huai'an First People's Hospital; GDP, gross domestic product; HPF, high-power field.

City A (Zhenjiang), City B (Nantong), and City C (Huai'an) correspond to the locations of the three participating hospitals−AHJU, AHNU, and HFPH−respectively.

Fig. 5 — **Correlation between city-level eosinophil counts and GDP**. (A–B) Mean peripheral blood eosinophil counts plotted against total GDP and GDP per capita, respectively. (C–D) Mean nasal mucosa eosinophil counts plotted against total GDP and GDP per capita, respectively. Spearman correlation coefficients (ρ) and P values are shown. *Abbreviations:* AHJU, Affiliated Hospital of Jiangsu University; AHNU, Affiliated Hospital of Nantong University; HFPH, Huai'an First People's Hospital; GDP, gross domestic product.

Association with PM2.5

Annual mean PM2.5 concentrations for the 3 cities (Zhenjiang, Nantong, and Huai'an) in 2008, 2012, and 2016 were extracted, and Spearman's correlation analyses were performed (eTable 3). Annual mean PM2.5 concentrations in all 3 cities showed a declining trend over the study period. In the pooled city-year analysis (n = 9), annual mean PM2.5 concentration was not significantly associated with either mean nasal mucosa eosinophil counts (ρ = −0.20, P = 0.606) or mean peripheral blood eosinophil counts (ρ = −0.617, P = 0.077) (eTable 3).

Discussion

This study investigated the correlation between peripheral blood eosinophil count and nasal mucosa eosinophil count in 1047 CRSwNP patients from Jiangsu Province, China. These hospitals are located in the northern, central, and southern regions of Jiangsu Province, making them geographically representative. To our knowledge, no similar reports have been published previously. Our results showed that, although peripheral blood eosinophil count was considered as a potential inflammatory marker, its correlation with nasal mucosa eosinophil count was weak, indicating its limited predictive ability for eosinophilic inflammation in the nasal mucosa. Additionally, we found notable regional and temporal variation in eosinophilic inflammation, suggesting that broader geographic and contextual factors may be relevant when interpreting local inflammatory profiles.

CRS has a complex, multifactorial etiology involving anatomical, infectious, genetic, and immune factors.31, 32, 33 Accurate endotyping of CRS, particularly eosinophilic CRS, is crucial for tailored treatment and prognosis prediction.³⁴ Tissue eosinophilia, typically assessed in nasal polyp specimens by eosinophil counts per high-power field, has been consistently associated with disease severity, recurrence, and treatment response.35, 36, 37 Peripheral blood eosinophil counts have also been proposed as a simpler potential marker for identifying eosinophilic inflammation.⁴ However, substantial controversy remains regarding the relationship between peripheral blood eosinophil levels and local tissue eosinophil burden. Gitomer et al reported that there was no significant correlation between peripheral blood eosinophil levels and nasal mucosa eosinophil count.³⁸ Similarly, Tecimer et al did not find a clear association between the eosinophilic nature of nasal polyp tissue and disease severity.³⁹ Consistent with these findings, our results demonstrated that peripheral blood eosinophil counts showed only a weak correlation with tissue eosinophil counts derived from nasal polyp specimens, limiting their ability to accurately predict local eosinophilic inflammation. This discrepancy likely reflects the heterogeneity of CRS and the influence of regional, demographic, and disease-related factors. Indeed, reported tissue eosinophil thresholds used to define eosinophilic CRS vary widely across studies and populations,²³^,⁴⁰^,⁴¹ underscoring the limitations of relying on a single blood-based marker to capture complex local inflammatory phenotypes.

In this study, we strictly standardized patient inclusion criteria, tissue processing protocols, and eosinophil counting procedures to minimize the impact of technical and procedural variability. Nevertheless, we observed distinct patterns of eosinophilic inflammation across hospitals, indicating that methodology alone does not account for the observed variation. Geographic heterogeneity in CRS–related inflammation has been documented previously.⁴² In a multicenter study involving patients with CRS from Europe, Asia, and Oceania, Wang et al reported marked regional differences in mucosa inflammatory profiles—particularly in the balance between eosinophilic versus neutrophilic inflammation and in type 2–related inflammatory markers—despite broadly comparable laboratory methodologies across centers.⁴² In addition, regional variation may be shaped by differences in aeroallergen exposure. A large multicenter study in China, which included patients with allergic symptoms from 52 cities, revealed substantial regional and seasonal variation in aeroallergen sensitization patterns.⁴³ Thus, although all 3 centers in our study were located within the same province, subtle differences in allergen exposure, climate, air quality, socioeconomic conditions, and healthcare resources may still exist and could collectively contribute to the observed heterogeneity in eosinophilic inflammation.

We further explored the potential contribution of city-level GDP in 2008, 2012, and 2016 to the observed heterogeneity in eosinophilic inflammation across cities. As a measure of a city's total economic output in a given year, GDP serves as a broad indicator of regional socioeconomic development and may reflect differences in urbanization levels, healthcare resources, diagnostic awareness, and living environments. Prior studies have suggested that socioeconomic factors are associated with CRS presentation and management.⁴⁴^,⁴⁵ Gill et al used the area deprivation index to assess neighborhood-level socioeconomic deprivation among patients with CRS and found that greater socioeconomic disadvantage was linked to worse baseline disease severity and a higher likelihood of prior endoscopic sinus surgery.⁴⁴ Similarly, Shah et al reported disparities in healthcare access among adults with CRS according to income, insurance status, and race.⁴⁵ In this context, our results suggest that GDP−used here as a broad proxy for regional economic development−may capture co-varying contextual factors influencing inflammatory patterns in CRSwNP, potentially including healthcare access, environmental exposures, and local lifestyle factors. Although exploratory, this finding offers an intriguing socioeconomic perspective on regional differences in eosinophilic inflammation.

Environmental exposure represents another important contextual factor that may contribute to regional heterogeneity. Air pollution, including fine particulate matter (PM2.5 and PM10) and gaseous pollutants such as SO₂, NO₂, and O₃, has been increasingly implicated in CRS onset and severity, although its relationship with inflammatory profiles in CRS appears complex.²⁷^,⁴⁶^,⁴⁷ Lower socioeconomic status may increase exposure to pollutants and exacerbate CRS severity, reflecting a potential interaction between social and environmental factors.⁴⁸ In our city-level analysis, annual mean PM2.5 concentrations decreased across the 3 cities from 2008 to 2016, suggesting an overall improvement in air quality; however, no significant associations were observed between PM2.5 and peripheral blood or nasal mucosa eosinophil counts. This likely reflects limitations of aggregated city-level measures, which cannot capture individual exposures, seasonal variation, short-term peaks, or indoor and occupational exposures. These results do not exclude a potential role of environmental factors in shaping inflammatory heterogeneity in CRSwNP, and future studies incorporating individual-level exposure assessment and multidimensional environmental data will be important to clarify these relationships.

Although this study provides insight into the relationship between peripheral blood and nasal mucosa eosinophil counts in CRSwNP, several limitations should be acknowledged. First, this study did not include other potential inflammatory markers, such as IL-5 and IgE, which may be closely associated with eosinophil levels and the severity of mucosa inflammation. Second, our study focused on surgical CRSwNP patients from a single province in China, which may affect the generalizability of findings to other disease severities or regions. Future studies incorporating multiple inflammatory biomarkers and broader patient populations will help to refine the clinical utility of eosinophil profiling in CRSwNP.

Conclusion

Peripheral blood eosinophil count is a poor surrogate for local tissue inflammation in CRSwNP. Regional variability in eosinophilic inflammation may be shaped by broader contextual factors, including socioeconomic and environmental conditions, highlighting the need to consider geographic heterogeneity in individualized disease assessment and management.

Ethics statement

Ethical approval was obtained from the ethics committees of all 3 participating hospitals (Approval IDs: KY2023K1104 for AHJU, 2023-K136-01 for AHNU, and KY-2023-179-01 for HFPH). Given the retrospective nature of the study and the use of anonymized electronic medical records, the requirement for informed consent was waived by the respective institutional review boards.

Consent for publication

All authors have approved the manuscript for submission. The participants understand that their personal information will not be published.

Author contribution

HSN, LYZ, and LC contributed to the concept and design of the study. HSN, LYZ, HQL, ZCZ, MZ, MPL, and LC were involved in the acquisition, analysis, or interpretation of data. HSN and LYZ performed the statistical analysis. HSN drafted the manuscript. LYZ and LC critically revised the manuscript for important intellectual content. LC and MPL provided administrative, technical, or material support. LC supervised the study. All authors read and approved the final manuscript.

Submission declaration

The authors confirm that this manuscript is original, has not been published previously, is not under consideration elsewhere, and has been approved by all authors.

Availability of data and material

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

Nothing to disclose.

Funding source

This work was supported by the Jiangsu Province Capability Improvement Project through Science, Technology and Education (JSDW202203) of China. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Declaration of competing interest

The authors declare that they have no relevant conflicts of interest related to this study.

Acknowledgments

The authors would like to express their sincere gratitude to colleagues from the Affiliated Hospital of Nantong University, the Affiliated Hospital of Jiangsu University, the Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, and the First Affiliated Hospital with Nanjing Medical University for their invaluable support and collaboration throughout this study. We also extend our heartfelt appreciation to all the patients whose clinical data made this research possible.

Footnotes

^{Appendix A}

Supplementary data to this article can be found online at https://doi.org/10.1016/j.waojou.2026.101440.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1

mmc1.docx^{(1.5MB, docx)}

References

1.Chegini Z., Didehdar M., Khoshbayan A., Karami J., Yousefimashouf M., Shariati A. The role of staphylococcus aureus enterotoxin b in chronic rhinosinusitis with nasal polyposis. Cell Commun Signal. 2022;20(1):29. doi: 10.1186/s12964-022-00839-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Min H.K., Lee S., Kim S., et al. Global incidence and prevalence of chronic rhinosinusitis: a systematic review. Clin Exp Allergy. 2025;55(1):52–66. doi: 10.1111/cea.14592. [DOI] [PubMed] [Google Scholar]
3.Fokkens W., Desrosiers M., Harvey R., et al. EPOS2020: development strategy and goals for the latest European position paper on rhinosinusitis. Rhinology. 2019;57(3):162–168. doi: 10.4193/Rhin19.080. [DOI] [PubMed] [Google Scholar]
4.Bayer K., Hamidovic S., Brkic F.F., Besser G., Mueller C.A., Liu D.T. Peripheral eosinophil count and eosinophil-to-lymphocyte ratio are associated with revision sinus surgery. Eur Arch Otorhinolaryngol. 2023;280(1):183–190. doi: 10.1007/s00405-022-07497-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Tajudeen B.A., Ganti A., Kuhar H.N., et al. The presence of eosinophil aggregates correlates with increased postoperative prednisone requirement. Laryngoscope. 2019;129(4):794–799. doi: 10.1002/lary.27693. [DOI] [PubMed] [Google Scholar]
6.Zhang R., Fu P., Tian H. The exploration of immunological landscape and drug targets in chronic rhinosinusitis with nasal polyps. World Allergy Organ J. 2026;19(1) doi: 10.1016/j.waojou.2025.101140. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Kato A., Schleimer R.P., Bleier B.S. Mechanisms and pathogenesis of chronic rhinosinusitis. J Allergy Clin Immunol. 2022;149(5):1491–1503. doi: 10.1016/j.jaci.2022.02.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Sedaghat A.R., Kuan E.C., Scadding G.K. Epidemiology of chronic rhinosinusitis: prevalence and risk factors. J Allergy Clin Immunol Pract. 2022;10(6):1395–1403. doi: 10.1016/j.jaip.2022.01.016. [DOI] [PubMed] [Google Scholar]
9.Gigon L., Fettrelet T., Yousefi S., Simon D., Simon H.U. Eosinophils from A to Z. Allergy. 2023;78(7):1810–1846. doi: 10.1111/all.15751. [DOI] [PubMed] [Google Scholar]
10.Ghaffari S., Rezaei N. Eosinophils in the tumor microenvironment: implications for cancer immunotherapy. J Transl Med. 2023;21(1):551. doi: 10.1186/s12967-023-04418-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Flinkman E., Vahatalo I., Tuomisto L.E., et al. Association between blood eosinophils and neutrophils with clinical features in adult-onset asthma. J Allergy Clin Immunol Pract. 2023;11(3):811–821.e815. doi: 10.1016/j.jaip.2022.11.025. [DOI] [PubMed] [Google Scholar]
12.Siddiqui S., Bachert C., Bjermer L., et al. Eosinophils and tissue remodeling: relevance to airway disease. J Allergy Clin Immunol. 2023;152(4):841–857. doi: 10.1016/j.jaci.2023.06.005. [DOI] [PubMed] [Google Scholar]
13.Yoo S.H., Mo J.H. The different features of inflammatory endotypes in korean patients with chronic rhinosinusitis. Allergy Asthma Immunol Res. 2023;15(4):413–415. doi: 10.4168/aair.2023.15.4.413. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Kim J., Kwak S., Lee J., et al. Eosinophilic chronic rhinosinusitis and pathogenic role of protease. Int J Mol Sci. 2023;24(24) doi: 10.3390/ijms242417372. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Ma L., Shi J., Wang K., Sun Y., Xu R. Clinical characteristics of patients with CRSwNP with intensely high eosinophil level. Laryngoscope Investig Otolaryngol. 2022;7(2):316–324. doi: 10.1002/lio2.758. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Miyabe Y., Fukuchi M., Tomizawa H., et al. Aggregated eosinophils and neutrophils characterize the properties of mucus in chronic rhinosinusitis. J Allergy Clin Immunol. 2024;153(5):1306–1318. doi: 10.1016/j.jaci.2023.11.925. [DOI] [PubMed] [Google Scholar]
17.Fokkens W.J., Lund V.J., Hopkins C., et al. European position paper on rhinosinusitis and nasal polyps 2020. Rhinology. 2020;58(Suppl S29):1–464. doi: 10.4193/Rhin20.600. [DOI] [PubMed] [Google Scholar]
18.Chen M., Xu Z., Fu Y., et al. A novel inflammatory endotype diagnostic model based on cytokines in chronic rhinosinusitis with nasal polyps. World Allergy Organ J. 2023;16(7) doi: 10.1016/j.waojou.2023.100796. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Jin J., Guo B., Zhang W., et al. Predictive value of eosinophil cationic protein in nasal secretions in eosinophilic chronic rhinosinusitis. Laryngoscope. 2023;133(12):3304–3312. doi: 10.1002/lary.30692. [DOI] [PubMed] [Google Scholar]
20.Kong W., Wu Q., Chen Y., et al. Chinese central compartment atopic disease: the clinical characteristics and cellular endotypes based on whole-slide imaging. J Asthma Allergy. 2022;15:341–352. doi: 10.2147/JAA.S350837. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Wang W., Gao Y., Zhu Z., et al. Changes in the clinical and histological characteristics of Chinese chronic rhinosinusitis with nasal polyps over 11 years. Int Forum Allergy Rhinol. 2019;9(2):149–157. doi: 10.1002/alr.22234. [DOI] [PubMed] [Google Scholar]
22.Wang K.H., Deng J., Yang M., et al. [Concordant systemic and local eosinophilia relates to poorer disease control in patients with nasal polyps] Zhonghua er bi yan hou tou jing wai ke za zhi. 2021;56(10):1042–1050. doi: 10.3760/cma.j.cn115330-20210428-00235. [DOI] [PubMed] [Google Scholar]
23.Yao Y., Xie S., Yang C., Zhang J., Wu X., Sun H. Biomarkers in the evaluation and management of chronic rhinosinusitis with nasal polyposis. Eur Arch Otorhinolaryngol. 2017;274(10):3559–3566. doi: 10.1007/s00405-017-4547-2. [DOI] [PubMed] [Google Scholar]
24.Vidal L., Dlamini Z., Qian S., et al. Equitable inclusion of diverse populations in oncology clinical trials: deterrents and drivers. ESMO Open. 2024;9(5) doi: 10.1016/j.esmoop.2024.103373. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Cotter E.W., Dunford A., Gilchrist K., Yan T., Deyton L., Essel K. Reckoning with the past: a qualitative analysis of medical students describing their formative experiences with weight bias. J Eat Disord. 2025;13(1):50. doi: 10.1186/s40337-025-01231-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Sweef O., Mahfouz R., Tascioglu T., et al. Decoding lncrna in copd: unveiling prognostic and diagnostic power and their driving role in lung cancer progression. Int J Mol Sci. 2024;25(16):9001. doi: 10.3390/ijms25169001. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Zhang Z.Q., Li J.Y., Guo Q., et al. Association between air pollution and allergic upper respiratory diseases: a meta-analysis. Eur Respir Rev. 2025;34(176) doi: 10.1183/16000617.0266-2024. 240266. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Subspecialty Group of Rhinology, Editorial Board of Chinese Journal of Otorhinolaryngology Head and Neck Surgery. Subspecialty Group of Rhinology, Society of Otorhinolaryngology Head and Neck Surgery, Chinese Medical Association Zhonghua Er Bi yan hou tou jing wai Ke Za zhi. 2013;48(2):92–94. [Guidelines for diagnosis and treatment of chronic rhinosinusitis (2012, Kunming)] [PubMed] [Google Scholar]
29.Liu H., Liu J., Li M., Gou P., Cheng Y. Assessing the evolution of PM2.5 and related health impacts resulting from air quality policies in China. Environ Impact Assess Rev. 2022;93 [Google Scholar]
30.Liu H. Science Data Bank; 2025. Annual Mean PM2.5 Concentration Dataset of 342 Chinese Cities (2000-2024)https://cstr.cn/31253.11.sciencedb.07104 [Google Scholar]
31.Lee R.J., Cohen N.A. Innate immune function in chronic rhinosinusitis. J Allergy Clin Immunol. 2025;155(5):1472–1474. doi: 10.1016/j.jaci.2025.01.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Chin C.J., Scott J.R., Lee J.M. Diagnosis and management of chronic rhinosinusitis. CMAJ (Can Med Assoc J) 2025;197(6):E148–E154. doi: 10.1503/cmaj.241101. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Cai S., Xu S., Zhao Y., Zhang L. Efficacy and safety of biologics for chronic rhinosinusitis with nasal polyps: a meta-analysis of real-world evidence. Allergy. 2025;80(5):1256–1270. doi: 10.1111/all.16499. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Kim D.H., Kim S.W., Basurrah M.A., Hwang S.H. Clinical and laboratory features of various criteria of eosinophilic chronic rhinosinusitis: a systematic review and meta-analysis. Clin Exp Otorhinolaryngol. 2022;15(3):230–246. doi: 10.21053/ceo.2022.00052. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Shin S.H., Ye M.K., Park J., Geum S.Y. Immunopathologic role of eosinophils in eosinophilic chronic rhinosinusitis. Int J Mol Sci. 2022;23(21) doi: 10.3390/ijms232113313. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Sharbel D., Li M., Unsal A.A., et al. Use of mucosa eosinophil count as a guide in the management of chronic rhinosinusitis. Int Forum Allergy Rhinol. 2020;10(4):474–480. doi: 10.1002/alr.22517. [DOI] [PubMed] [Google Scholar]
37.Morawska-Kochman M., Smieszek A., Marcinkowska K., et al. Expression of apoptosis-related biomarkers in inflamed nasal sinus epithelium of patients with chronic rhinosinusitis with nasal polyps (CRSwNP)-evaluation at mRNA and miRNA levels. Biomedicines. 2022;10(6):1400. doi: 10.3390/biomedicines10061400. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Gitomer S.A., Fountain C.R., Kingdom T.T., et al. Clinical examination of tissue eosinophilia in patients with chronic rhinosinusitis and nasal polyposis. Otolaryngol Head Neck Surg. 2016;155(1):173–178. doi: 10.1177/0194599816637856. [DOI] [PubMed] [Google Scholar]
39.Tecimer S.H., Kasapoglu F., Demir U.L., Ozmen O.A., Coskun H., Basut O. Correlation between clinical findings and eosinophil/neutrophil ratio in patients with nasal polyps. Eur Arch Otorhinolaryngol. 2015;272(4):915–921. doi: 10.1007/s00405-014-3174-4. [DOI] [PubMed] [Google Scholar]
40.Brunet A., Milara J., Frias S., Cortijo J., Armengot M. Molecular and clinical predictors of quality of life in chronic rhinosinusitis with nasal polyps. J Clin Med. 2023;12(4):1391. doi: 10.3390/jcm12041391. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Li C., Zhang B., Yan M., et al. Cluster analysis of patients with chronic rhinosinusitis and asthma after endoscopic sinus surgery. Ann Allergy Asthma Immunol. 2023;130(3):325–332.e327. doi: 10.1016/j.anai.2022.11.013. [DOI] [PubMed] [Google Scholar]
42.Wang X., Zhang N., Bo M., et al. Diversity of TH cytokine profiles in patients with chronic rhinosinusitis: a multicenter study in Europe, Asia, and oceania. J Allergy Clin Immunol. 2016;138(5):1344–1353. doi: 10.1016/j.jaci.2016.05.041. [DOI] [PubMed] [Google Scholar]
43.Luo W., Wang D., Zhang T., et al. Prevalence patterns of allergen sensitization by region, gender, age, and season among patients with allergic symptoms in mainland China: a four-year multicenter study. Allergy. 2021;76(2):589–593. doi: 10.1111/all.14597. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Gill A.S., Tullis B., Mace J.C., et al. Health care disparities and chronic rhinosinusitis: does neighborhood disadvantage impact outcomes in sinonasal disease? Int Forum Allergy Rhinol. 2024;14(8):1302–1313. doi: 10.1002/alr.23337. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Shah J.P., Youn G.M., Wei E.X., Patel Z.M. Disparities in access to health care in adults with sinusitis in the United States. Int Forum Allergy Rhinol. 2023;13(11):2018–2029. doi: 10.1002/alr.23167. [DOI] [PubMed] [Google Scholar]
46.Chen M., Xu Z., Fu Y., et al. Association of long-term air pollution and allergen exposure with endotype shift in chronic rhinosinusitis with nasal polyps. Ann Allergy Asthma Immunol. 2025;135(2):171–179.e172. doi: 10.1016/j.anai.2025.03.014. [DOI] [PubMed] [Google Scholar]
47.Yang H.H., O'Sharkey K., Paul K., et al. Traffic and industrial pollutants and chronic rhinosinusitis. JAMA Otolaryngol Head Neck Surg. 2026 doi: 10.1001/jamaoto.2026.1176. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Velasquez N., Gardiner L., Cheng T.Z., et al. Relationship between socioeconomic status, exposure to airborne pollutants, and chronic rhinosinusitis disease severity. Int Forum Allergy Rhinol. 2022;12(2):172–180. doi: 10.1002/alr.22884. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1

mmc1.docx^{(1.5MB, docx)}

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

[bib1] 1.Chegini Z., Didehdar M., Khoshbayan A., Karami J., Yousefimashouf M., Shariati A. The role of staphylococcus aureus enterotoxin b in chronic rhinosinusitis with nasal polyposis. Cell Commun Signal. 2022;20(1):29. doi: 10.1186/s12964-022-00839-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib2] 2.Min H.K., Lee S., Kim S., et al. Global incidence and prevalence of chronic rhinosinusitis: a systematic review. Clin Exp Allergy. 2025;55(1):52–66. doi: 10.1111/cea.14592. [DOI] [PubMed] [Google Scholar]

[bib3] 3.Fokkens W., Desrosiers M., Harvey R., et al. EPOS2020: development strategy and goals for the latest European position paper on rhinosinusitis. Rhinology. 2019;57(3):162–168. doi: 10.4193/Rhin19.080. [DOI] [PubMed] [Google Scholar]

[bib4] 4.Bayer K., Hamidovic S., Brkic F.F., Besser G., Mueller C.A., Liu D.T. Peripheral eosinophil count and eosinophil-to-lymphocyte ratio are associated with revision sinus surgery. Eur Arch Otorhinolaryngol. 2023;280(1):183–190. doi: 10.1007/s00405-022-07497-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib5] 5.Tajudeen B.A., Ganti A., Kuhar H.N., et al. The presence of eosinophil aggregates correlates with increased postoperative prednisone requirement. Laryngoscope. 2019;129(4):794–799. doi: 10.1002/lary.27693. [DOI] [PubMed] [Google Scholar]

[bib6] 6.Zhang R., Fu P., Tian H. The exploration of immunological landscape and drug targets in chronic rhinosinusitis with nasal polyps. World Allergy Organ J. 2026;19(1) doi: 10.1016/j.waojou.2025.101140. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Kato A., Schleimer R.P., Bleier B.S. Mechanisms and pathogenesis of chronic rhinosinusitis. J Allergy Clin Immunol. 2022;149(5):1491–1503. doi: 10.1016/j.jaci.2022.02.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib8] 8.Sedaghat A.R., Kuan E.C., Scadding G.K. Epidemiology of chronic rhinosinusitis: prevalence and risk factors. J Allergy Clin Immunol Pract. 2022;10(6):1395–1403. doi: 10.1016/j.jaip.2022.01.016. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Gigon L., Fettrelet T., Yousefi S., Simon D., Simon H.U. Eosinophils from A to Z. Allergy. 2023;78(7):1810–1846. doi: 10.1111/all.15751. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Ghaffari S., Rezaei N. Eosinophils in the tumor microenvironment: implications for cancer immunotherapy. J Transl Med. 2023;21(1):551. doi: 10.1186/s12967-023-04418-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib11] 11.Flinkman E., Vahatalo I., Tuomisto L.E., et al. Association between blood eosinophils and neutrophils with clinical features in adult-onset asthma. J Allergy Clin Immunol Pract. 2023;11(3):811–821.e815. doi: 10.1016/j.jaip.2022.11.025. [DOI] [PubMed] [Google Scholar]

[bib12] 12.Siddiqui S., Bachert C., Bjermer L., et al. Eosinophils and tissue remodeling: relevance to airway disease. J Allergy Clin Immunol. 2023;152(4):841–857. doi: 10.1016/j.jaci.2023.06.005. [DOI] [PubMed] [Google Scholar]

[bib13] 13.Yoo S.H., Mo J.H. The different features of inflammatory endotypes in korean patients with chronic rhinosinusitis. Allergy Asthma Immunol Res. 2023;15(4):413–415. doi: 10.4168/aair.2023.15.4.413. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] 14.Kim J., Kwak S., Lee J., et al. Eosinophilic chronic rhinosinusitis and pathogenic role of protease. Int J Mol Sci. 2023;24(24) doi: 10.3390/ijms242417372. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] 15.Ma L., Shi J., Wang K., Sun Y., Xu R. Clinical characteristics of patients with CRSwNP with intensely high eosinophil level. Laryngoscope Investig Otolaryngol. 2022;7(2):316–324. doi: 10.1002/lio2.758. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib16] 16.Miyabe Y., Fukuchi M., Tomizawa H., et al. Aggregated eosinophils and neutrophils characterize the properties of mucus in chronic rhinosinusitis. J Allergy Clin Immunol. 2024;153(5):1306–1318. doi: 10.1016/j.jaci.2023.11.925. [DOI] [PubMed] [Google Scholar]

[bib17] 17.Fokkens W.J., Lund V.J., Hopkins C., et al. European position paper on rhinosinusitis and nasal polyps 2020. Rhinology. 2020;58(Suppl S29):1–464. doi: 10.4193/Rhin20.600. [DOI] [PubMed] [Google Scholar]

[bib18] 18.Chen M., Xu Z., Fu Y., et al. A novel inflammatory endotype diagnostic model based on cytokines in chronic rhinosinusitis with nasal polyps. World Allergy Organ J. 2023;16(7) doi: 10.1016/j.waojou.2023.100796. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib19] 19.Jin J., Guo B., Zhang W., et al. Predictive value of eosinophil cationic protein in nasal secretions in eosinophilic chronic rhinosinusitis. Laryngoscope. 2023;133(12):3304–3312. doi: 10.1002/lary.30692. [DOI] [PubMed] [Google Scholar]

[bib20] 20.Kong W., Wu Q., Chen Y., et al. Chinese central compartment atopic disease: the clinical characteristics and cellular endotypes based on whole-slide imaging. J Asthma Allergy. 2022;15:341–352. doi: 10.2147/JAA.S350837. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Wang W., Gao Y., Zhu Z., et al. Changes in the clinical and histological characteristics of Chinese chronic rhinosinusitis with nasal polyps over 11 years. Int Forum Allergy Rhinol. 2019;9(2):149–157. doi: 10.1002/alr.22234. [DOI] [PubMed] [Google Scholar]

[bib22] 22.Wang K.H., Deng J., Yang M., et al. [Concordant systemic and local eosinophilia relates to poorer disease control in patients with nasal polyps] Zhonghua er bi yan hou tou jing wai ke za zhi. 2021;56(10):1042–1050. doi: 10.3760/cma.j.cn115330-20210428-00235. [DOI] [PubMed] [Google Scholar]

[bib23] 23.Yao Y., Xie S., Yang C., Zhang J., Wu X., Sun H. Biomarkers in the evaluation and management of chronic rhinosinusitis with nasal polyposis. Eur Arch Otorhinolaryngol. 2017;274(10):3559–3566. doi: 10.1007/s00405-017-4547-2. [DOI] [PubMed] [Google Scholar]

[bib24] 24.Vidal L., Dlamini Z., Qian S., et al. Equitable inclusion of diverse populations in oncology clinical trials: deterrents and drivers. ESMO Open. 2024;9(5) doi: 10.1016/j.esmoop.2024.103373. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Cotter E.W., Dunford A., Gilchrist K., Yan T., Deyton L., Essel K. Reckoning with the past: a qualitative analysis of medical students describing their formative experiences with weight bias. J Eat Disord. 2025;13(1):50. doi: 10.1186/s40337-025-01231-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Sweef O., Mahfouz R., Tascioglu T., et al. Decoding lncrna in copd: unveiling prognostic and diagnostic power and their driving role in lung cancer progression. Int J Mol Sci. 2024;25(16):9001. doi: 10.3390/ijms25169001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27.Zhang Z.Q., Li J.Y., Guo Q., et al. Association between air pollution and allergic upper respiratory diseases: a meta-analysis. Eur Respir Rev. 2025;34(176) doi: 10.1183/16000617.0266-2024. 240266. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib28] 28.Subspecialty Group of Rhinology, Editorial Board of Chinese Journal of Otorhinolaryngology Head and Neck Surgery. Subspecialty Group of Rhinology, Society of Otorhinolaryngology Head and Neck Surgery, Chinese Medical Association Zhonghua Er Bi yan hou tou jing wai Ke Za zhi. 2013;48(2):92–94. [Guidelines for diagnosis and treatment of chronic rhinosinusitis (2012, Kunming)] [PubMed] [Google Scholar]

[bib29] 29.Liu H., Liu J., Li M., Gou P., Cheng Y. Assessing the evolution of PM2.5 and related health impacts resulting from air quality policies in China. Environ Impact Assess Rev. 2022;93 [Google Scholar]

[bib30] 30.Liu H. Science Data Bank; 2025. Annual Mean PM2.5 Concentration Dataset of 342 Chinese Cities (2000-2024)https://cstr.cn/31253.11.sciencedb.07104 [Google Scholar]

[bib31] 31.Lee R.J., Cohen N.A. Innate immune function in chronic rhinosinusitis. J Allergy Clin Immunol. 2025;155(5):1472–1474. doi: 10.1016/j.jaci.2025.01.022. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Chin C.J., Scott J.R., Lee J.M. Diagnosis and management of chronic rhinosinusitis. CMAJ (Can Med Assoc J) 2025;197(6):E148–E154. doi: 10.1503/cmaj.241101. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib33] 33.Cai S., Xu S., Zhao Y., Zhang L. Efficacy and safety of biologics for chronic rhinosinusitis with nasal polyps: a meta-analysis of real-world evidence. Allergy. 2025;80(5):1256–1270. doi: 10.1111/all.16499. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib34] 34.Kim D.H., Kim S.W., Basurrah M.A., Hwang S.H. Clinical and laboratory features of various criteria of eosinophilic chronic rhinosinusitis: a systematic review and meta-analysis. Clin Exp Otorhinolaryngol. 2022;15(3):230–246. doi: 10.21053/ceo.2022.00052. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib35] 35.Shin S.H., Ye M.K., Park J., Geum S.Y. Immunopathologic role of eosinophils in eosinophilic chronic rhinosinusitis. Int J Mol Sci. 2022;23(21) doi: 10.3390/ijms232113313. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib36] 36.Sharbel D., Li M., Unsal A.A., et al. Use of mucosa eosinophil count as a guide in the management of chronic rhinosinusitis. Int Forum Allergy Rhinol. 2020;10(4):474–480. doi: 10.1002/alr.22517. [DOI] [PubMed] [Google Scholar]

[bib37] 37.Morawska-Kochman M., Smieszek A., Marcinkowska K., et al. Expression of apoptosis-related biomarkers in inflamed nasal sinus epithelium of patients with chronic rhinosinusitis with nasal polyps (CRSwNP)-evaluation at mRNA and miRNA levels. Biomedicines. 2022;10(6):1400. doi: 10.3390/biomedicines10061400. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib38] 38.Gitomer S.A., Fountain C.R., Kingdom T.T., et al. Clinical examination of tissue eosinophilia in patients with chronic rhinosinusitis and nasal polyposis. Otolaryngol Head Neck Surg. 2016;155(1):173–178. doi: 10.1177/0194599816637856. [DOI] [PubMed] [Google Scholar]

[bib39] 39.Tecimer S.H., Kasapoglu F., Demir U.L., Ozmen O.A., Coskun H., Basut O. Correlation between clinical findings and eosinophil/neutrophil ratio in patients with nasal polyps. Eur Arch Otorhinolaryngol. 2015;272(4):915–921. doi: 10.1007/s00405-014-3174-4. [DOI] [PubMed] [Google Scholar]

[bib40] 40.Brunet A., Milara J., Frias S., Cortijo J., Armengot M. Molecular and clinical predictors of quality of life in chronic rhinosinusitis with nasal polyps. J Clin Med. 2023;12(4):1391. doi: 10.3390/jcm12041391. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib41] 41.Li C., Zhang B., Yan M., et al. Cluster analysis of patients with chronic rhinosinusitis and asthma after endoscopic sinus surgery. Ann Allergy Asthma Immunol. 2023;130(3):325–332.e327. doi: 10.1016/j.anai.2022.11.013. [DOI] [PubMed] [Google Scholar]

[bib42] 42.Wang X., Zhang N., Bo M., et al. Diversity of TH cytokine profiles in patients with chronic rhinosinusitis: a multicenter study in Europe, Asia, and oceania. J Allergy Clin Immunol. 2016;138(5):1344–1353. doi: 10.1016/j.jaci.2016.05.041. [DOI] [PubMed] [Google Scholar]

[bib43] 43.Luo W., Wang D., Zhang T., et al. Prevalence patterns of allergen sensitization by region, gender, age, and season among patients with allergic symptoms in mainland China: a four-year multicenter study. Allergy. 2021;76(2):589–593. doi: 10.1111/all.14597. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib44] 44.Gill A.S., Tullis B., Mace J.C., et al. Health care disparities and chronic rhinosinusitis: does neighborhood disadvantage impact outcomes in sinonasal disease? Int Forum Allergy Rhinol. 2024;14(8):1302–1313. doi: 10.1002/alr.23337. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib45] 45.Shah J.P., Youn G.M., Wei E.X., Patel Z.M. Disparities in access to health care in adults with sinusitis in the United States. Int Forum Allergy Rhinol. 2023;13(11):2018–2029. doi: 10.1002/alr.23167. [DOI] [PubMed] [Google Scholar]

[bib46] 46.Chen M., Xu Z., Fu Y., et al. Association of long-term air pollution and allergen exposure with endotype shift in chronic rhinosinusitis with nasal polyps. Ann Allergy Asthma Immunol. 2025;135(2):171–179.e172. doi: 10.1016/j.anai.2025.03.014. [DOI] [PubMed] [Google Scholar]

[bib47] 47.Yang H.H., O'Sharkey K., Paul K., et al. Traffic and industrial pollutants and chronic rhinosinusitis. JAMA Otolaryngol Head Neck Surg. 2026 doi: 10.1001/jamaoto.2026.1176. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib48] 48.Velasquez N., Gardiner L., Cheng T.Z., et al. Relationship between socioeconomic status, exposure to airborne pollutants, and chronic rhinosinusitis disease severity. Int Forum Allergy Rhinol. 2022;12(2):172–180. doi: 10.1002/alr.22884. [DOI] [PubMed] [Google Scholar]

PERMALINK

Socioeconomic and regional influences on eosinophilic inflammation in chronic rhinosinusitis with nasal polyps

Hao-Sheng Ni, MD

Li-Yuan Zhang, MD

Han-Qiang Lu, MD

Zhi-Cun Zhang, MD

Min Zhang, MD

Mei-Ping Lu, MD, PhD

Lei Cheng, MD, PhD

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Study design and subjects

Eosinophil counting

City-level GDP and PM2.5 data sources

Statistical analysis

Results

Demographic characteristics

Table 1.

Regional variations in peripheral blood and nasal mucosa eosinophil counts

Table 2.

Fig. 1.

Temporal trends in peripheral blood and nasal mucosa eosinophil counts

Fig. 2.

Sex- and age-based comparisons

Correlation analysis

Fig. 3.

ROC curve analysis

Linear regression analysis

Fig. 4.

Association with GDP

Table 3.

Fig. 5.

Association with PM2.5

Discussion

Conclusion

Ethics statement

Consent for publication

Author contribution

Submission declaration

Availability of data and material

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

Funding source

Declaration of competing interest

Acknowledgments

Footnotes

Appendix A. Supplementary data

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases