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Abstract

Objective

Allergic rhinitis (AR) is a common inflammatory condition that affects quality of life. Frailty, a syndrome of reduced physiological reserves, has been linked to various chronic diseases, but its association with AR remains unclear. This study aimed to explore the relationship between frailty and AR in a large-scale, population-based cohort.

Methods

In this retrospective cross-sectional study, 24,269 participants from the Korea National Health and Nutrition Examination Survey were categorized into AR and non-AR groups based on self-reported history. Frailty was assessed using the modified frailty phenotype (FP) and frailty index (FI). Multivariate logistic regression analysis was used to assess the association, with subgroup analyses performed by demographic and clinical factors.

Results

Frailty measured by FP (OR = 1.251, 95% CI [1.060, 1.476]) and FI (OR: 1.587, 95% CI [1.116, 2.257]) was associated with an increased risk of AR, particularly pronounced in males (OR = 1.516, 95% CI [1.134, 2.027]), younger (OR = 1.298, 95% CI [1.022, 1.648]) and middle-aged adults (OR = 1.372, 95% CI [1.024, 1.838]), individuals with a high school education (OR = 1.482, 95% CI [1.135, 1.934]), those with a body mass index ≥ 25 (OR = 1.823, 95% CI [1.388, 2.395]), and white-collar workers (OR = 1.361, 95% CI [1.051, 1.761]), and those with higher family income (OR = 1.433, 95% CI [1.072, 1.914]), a history of smoking (OR = 1.265, 95% CI [1.040, 1.535]), and frequent alcohol consumption (OR = 1.578, 95% CI [1.091, 2.282]). Further analysis indicated that the heightened risk of AR associated with frailty may be attributed to increased levels of emotional exhaustion (OR: 1.334, 95% CI [1.178, 1.510]).

Conclusions

Frailty is independently associated with a higher prevalence of AR, highlighting the need for comprehensive management strategies addressing both physical and mental health in AR patients.

Keywords

Frailty allergic rhinitis cross-sectional study KNHANES logistic regression analysis

Introduction

Allergic rhinitis (AR) is a globally prevalent respiratory disease, with an estimated global prevalence exceeding 500 million people, affecting 20%–40% of the population, and its incidence is increasing with the process of industrialization.^1,2 AR not only severely impacts the quality of life of patients but is also significantly associated with the socioeconomic burden.³ Although AR has a certain prevalence across all age groups,^4,5 research on its age-related characteristics remains underexplored. Traditionally, allergic conditions are thought to be more prevalent in younger populations, with a gradual decline in prevalence observed with ageing.⁶ However, age-related structural and physiological changes in the nasal mucosa may aggravate AR symptoms, such as postnasal drip, congestion, and cough, in older adults.⁷ Importantly, chronological age alone may not fully capture the heterogeneity in health status across individuals.

Frailty is a common phenomenon in the ageing process, characterized by a decline in physiological reserves and functional capacity, making individuals more susceptible to negative health outcomes when exposed to acute stressors.⁸ Among the various operational definitions of frailty, the Fried frailty phenotype (FP) is one of the most widely adopted models in epidemiological studies.⁹ It conceptualizes frailty as a biological syndrome characterized by weakness, unintentional weight loss, low level of physical activity, emotional exhaustion, and slowness. This phenotype-based approach captures the multisystemic nature of frailty and has been validated in diverse populations for its predictive value regarding adverse health outcomes.

The prevalence of frailty increases with age and is closely related to various lower respiratory diseases, including interstitial lung disease (ILD), asthma, and pleural diseases due to chronic low-grade inflammation in the body.¹⁰ Verduri et al.¹¹ found that frailty is highly prevalent in patients with ILD, with a median prevalence of 48%, and is associated with increased long-term mortality and hospitalization rates. In contrast, frailty prevalence in asthma was lower (median 9.5%), but data on its clinical outcomes remain limited. These findings underscore the need for further investigation into frailty's role in chronic respiratory conditions to improve patient management and outcomes. Therefore, incorporating the concept of frailty may be more appropriate than considering age alone when assessing the impact of age on AR. Nonetheless, research on the relationship between AR and FP is still rare. This study aimed to utilize the Korea National Health and Nutrition Examination Survey (KNHANES) database to explore the relationship between AR and FP in the population, with the goal of providing a new perspective for the prevention and treatment of AR.

Materials and methods

Study design and participants

The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.¹² KNHANES is a national, multi-stage, stratified, cluster probability sampling health survey designed to assess the health status, health-related behaviors, and nutritional status of adults in Korea.¹³ This is a retrospective cross-sectional study based on data from the seventh KNHANES conducted between 2016 and 2018. A total of 24,269 participants were initially enrolled in this study. After excluding 2013 individuals without data on the AR survey, 5539 without data on the modified FP assessment, and 11,747 without data on the frailty index (FI) assessment, two analytic cohorts were established: 16,717 participants with both AR survey and FP assessment data, and 10,509 participants with both AR survey and FI assessment data (Figure 1). All participants provided written informed consent, and this study was approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention (KCDC) (No: 2018-01-03-P-A). All procedures were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki Declaration, as revised in 2024. This study used deidentified, publicly available data from KNHANES, and all patient details were fully anonymized prior to analysis to ensure that individuals could not be identified in any way.

Figure 1.

Flowchart of the study population.

Definitions of AR

In this study, all participants were queried about their history of AR. Individuals who had received an AR diagnosis from professional healthcare providers were identified as AR patients as described in previous study.¹⁴ All participants were then categorized into non-AR and AR groups for subsequent analysis.

Definitions of frailty

In this study, frailty status was considered an exposure factor and assessed using the FI and the FP revised by Fried,¹⁵ due to their compatibility with the available variables in the KNHANES dataset and its widespread use and validation in large-scale epidemiological research.^9,16 The specific evaluation criteria are as follows: (1) Weakness: Based on the 2014 Asian Working Group for Sarcopenia standards, a handgrip strength of less than 26 kg for males and less than 18 kg for females¹⁷; (2) Unintentional weight loss: Self-reported weight loss exceeding 3 kg in the past year¹⁸; (3) Low level of physical activity: Engaging in less than 2 h of moderate-intensity physical activity or less than 1 h of high-intensity physical activity per week¹⁹; (4) Emotional exhaustion: Participants expressed fatigue with the statement “I feel very tired” in the context of perceived stress levels²⁰; (5) Slowness: In the mobility domain of the European Quality of Life-5 Dimensions, a positive response to at least one of the questions “I have slight difficulty walking” or “I need to stay in bed all day."²¹ According to these criteria, participants meeting zero to two conditions are classified as “Nonfrailty,” whereas those meeting three or more conditions are deemed “Frailty.”

FI was derived as the ratio of the number of existing health deficits to the total number of assessed deficits.²² Detailed components and scoring criteria are provided in Supplemental Table S1. Each deficit was scored from 0 (absence of deficit) to 1 (full expression of deficit), following a standardized approach based on variables available in the KNHANES and consistent with established methodologies in the literature.^23,24 Frailty status was classified using conventional cutoff points: FI < 0.25 as nonfrail and FI ≥ 0.25 as frail. Deficits with a prevalence below 1% (e.g. tuberculosis, liver cirrhosis) or with more than 20% missing data (e.g. hospitalization history) were excluded from the analysis. Participants with fewer than 30 recorded deficit variables were deemed to have insufficient data for FI calculation.

Confounders assessment

In this study, confounding factors included demographic characteristics, lifestyle, and health-related variables (Figure 2). Sociodemographic features comprised gender (male/female), age (< 40/41–60/≥ 61 years), place of residence (urban/rural), family income (divided into quartiles), educational level (below high school, high school graduate, college degree or above), and occupational category. Occupations were categorized into four distinct types based on professional characteristics: white-collar, blue-collar, green-collar, and unemployed.¹⁴ White-collar jobs consist of managers, service industry workers, professionals, administrative staff, and sales positions; blue-collar occupations are primarily include skilled operators, including machinery and equipment operators, machine tool operators, and assemblers; green-collar jobs involve skilled workers in the fields of forestry, agriculture, and fisheries. Lifestyle assessment included smoking (lifetime consumption of < 5 packs vs ≥ 5 packs) and alcohol consumption (less than twice a week vs twice or more per week). Health-related variables considered were the presence of asthma and body mass index (BMI), calculated as weight (kg) divided by the square of height (m²), with a cutoff point of 25, classifying participants into nonobese (BMI < 25) and obese groups (BMI ≥ 25).

Figure 2.

Variables included in this study.

Statistical analysis

Data analysis for this study was performed using IBM SPSS software. In line with the statistical guidelines issued by KCDC, a complex sample analysis method was utilized to appropriately process the weighted KNHANES data. Categorical variables were depicted as frequencies and percentages, while continuous variables were represented by means ± standard deviations. To evaluate the association between frailty and AR, we employed multivariable logistic regression for complex samples, reporting odds ratios (ORs) and 95% confidence intervals (CIs). All models were adjusted for relevant covariates, and the main effect p-value for frailty was corrected for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure, based on values obtained from the complex samples models (Supplemental Table S2). To explore potential effect modification, each covariate was tested for interaction with frailty by including an individual frailty × covariate term in the model. The p-values for these interaction terms were also adjusted using the FDR method. Covariates with an FDR-adjusted p-value < .05 (Supplemental Table S3) for interaction were then included in stratified analyses to further examine heterogeneity in the frailty–AR association. All FDR adjustments were performed outside SPSS using the original model outputs. A two-sided p-value < .05 was considered statistically significant.

Results

Basic information of study participants

This study included a total of 16,717 participants with a mean age of 51.01 ± 16.76 years. In terms of gender distribution, males comprised 44.3%, while females accounted for 55.7%. The majority of participants reported lifetime smoking of less than five packs (62.1%), alcohol consumption less than twice per week (74.9%), a BMI below 25 kg/m² (65.2%), and 97.0% had no history of asthma. Among all participants, 14.3% were diagnosed with AR, and 15.9% were classified as frail based on the modified FP criteria, corresponding to weighted estimates of 16.4 million and 18.2 million individuals in the Korean population, respectively. Using the FI criteria (n = 10,509), 10.8% of participants had AR, and 9.6% were diagnosed as frail, translating to approximately 7.8 million and 6.9 million individuals nationwide (Table 1).

Table 1.

Characteristics of the study population.

Characteristics	Frailty index (N = 10,509)				Frailty phenotype (N = 16,717)
Characteristics	N (%)	Weighted N in millions	Nonallergic rhinitis^a	Allergic rhinitis^b	N (%)	Weighted N in millions	Nonallergic rhinitis^c	Allergic rhinitis^d
Age (years), mean (SE)	58.78 ± 11.54	72.0	59.31 ± 11.54	54.44 ± 10.65	51.01 ± 16.76	114.5	52.30 ± 16.63	43.27 ± 15.39
<40 years old	257 (2.5%)	1.8	211 (2.3%)	46 (4.0%)	5031 (30.1%)	34.5	3879 (27.1%)	1152 (48.1%)
41–60 years old	5677 (54.0%)	38.9	4898 (52.3%)	779 (68.3%)	6297 (37.7%)	43.2	5428 (37.9%)	869 (36.3%)
≥61 years old	4575 (43.5%)	31.3	4259 (45.4%)	316 (27.7%)	5389 (32.2%)	36.9	5013 (35.0%)	376 (15.7%)
Gender, N (%)
Male	4705 (44.8%)	32.2	4277 (45.7%)	428 (37.5%)	7408 (44.3%)	50.7	6509 (45.5%)	899 (37.5%)
Female	5804 (55.2%)	39.8	5091 (54.3%)	713 (62.5%)	9309 (55.7%)	63.8	7811 (54.5%)	1498 (62.5%)
Educational level, N (%)
Below high school	4142 (39.5%)	28.4	3859 (41.3%)	283 (24.8%)	4983 (29.9%)	34.2	4625 (32.4%)	358 (15.0%)
High school graduate	3247 (30.9%)	22.2	2861 (30.6%)	386 (33.9%)	5381 (32.2%)	36.9	4533 (31.7%)	848 (35.4%)
Bachelor degree or higher	3102 (29.6%)	21.3	2631 (28.1%)	471 (41.3%)	6323 (37.9%)	43.4	5135 (35.9%)	1188 (49.6%)
Smoking, N (%)
<5 pack in a lifetime	6345 (60.4%)	43.5	5594 (59.7%)	751 (65.8%)	10386 (62.1%)	71.1	8772 (61.3%)	1614 (67.3%)
≥5 pack in a lifetime	4161 (39.6%)	28.5	3771 (40.3%)	390 (34.2%)	6327 (37.9%)	43.4	5544 (38.7%)	783 (32.7%)
Alcohol intake, N (%)
<2 times a week	6800 (74.5%)	46.6	5983 (63.9%)	817 (78.8%0	11177 (74.9%)	85.8	9432 (74.4%)	1745 (77.7%)
≥2 times a week	2331 (25.5%)	16.0	2111 (26.1%)	220 (21.2%)	3749 (25.1%)	28.7	3248 (25.6%)	501 (22.3%)
BMI (Kg/m²), N (%)
<25	6593 (62.7%)	45.2	5840 (62.3%)	753 (66.0%)	10867 (65.2%)	74.7	9179 (64.3%)	1688 (70.6%)
≥25	3916 (37.3%)	26.8	3528 (37.7%)	388 (34.0%)	5807 (34.8%)	39.8	5103 (35.7%)	704 (29.4%)
Asthma, N (%)
No	10218 (97.2%)	70.0	9148 (97.7%)	1070 (93.8%)	16219 (97.0%)	111.1	13991 (97.7%)	2228 (92.9%)
Yes	291 (2.8%)	2.0	220 (2.3%)	71 (6.2%)	498 (3.0%)	3.4	329 (2.3%)	169 (7.1%)
Frailty status
No	9503 (90.4%)	65.1	8454 (90.2%)	1049 (91.9%)	14060 (84.1%)	96.3	12027 (87.6%)	2033 (84.8%)
Yes	1006 (9.6%)	6.9	914 (9.8%)	92 (8.1%)	2657 (15.9%)	18.2	2293 (12.4%)	364 (15..2%)
Weight loss
No					14621 (87.5%)	100.2	12538 (87.6%)	2083 (86.9%)
Yes					2096 (12.5%)	14.3	1782 (12.4%)	314 (13.1%)
Weakness
No					13698 (81.9%)	93.8	11635 (81.3%)	2063 (86.1%)
Yes					3019 (18.1%)	20.7	2685 (18.7%)	334 (13.9%)
Slowness
No					14437 (86.4%)	98.9	12260 (85.6%)	2177 (90.8%)
Yes					2280 (13.6%)	15.6	2060 (14.4%)	220 (9.2%)
Exhaustion
No					12312 (73.6%)	84.3	10760 (75.1%)	1552 (64.7%)
Yes					4405 (26.4%)	30.2	3560 (24.9%)	845 (35.3%)
Low physical activity
No					2681 (16.0%)	18.3	2185 (15.3%)	496 (20.70%)
Yes					14036 (84.0%)	96.2	12135 (84.7%)	1901 (79.3%)

Note: ^aNonallergic rhinitis: N = 9368; weighted N in millions = 64.2.

Allergic rhinitis: N = 1141; weighted N in millions = 7.8.

Nonallergic rhinitis: N = 14320; weighted N in millions = 98.0.

Allergic rhinitis: N = 2397; weighted N in millions = 16.4.

Multivariate analysis

To investigate the association between frailty (assessed by modified FP) and AR, logistic regression analyses for complex samples were performed using two models: Model 3: adjusted for all covariates excluding asthma, Model 4: adjusted for all covariates including asthma. The results indicated that frailty was associated with an increased risk of AR in both models, with slight differences in the ORs. In Model 3, frailty was associated with a higher risk of AR (OR = 1.268, 95% CI [1.074, 1.497]), while in Model 4, the OR was slightly lower (OR = 1.251, 95% CI [1.060, 1.476]). This suggests that the inclusion of asthma in the model did not substantially alter the direction of the association between frailty and AR, but resulted in a slightly reduced effect size (Figure 3). Additionally, after adjusting for multiple comparisons using the Benjamini–Hochberg FDR procedure, the p-value for frailty remained statistically significant, with an FDR-adjusted p-value of .015 compared to the original p-value of .008. These findings reinforce the robustness of the observed association between frailty and AR (Supplemental Table S2).

Figure 3.

Logistic analysis for the association between frailty and allergic rhinitis.

Similarly, when frailty was assessed using the FI criteria, a significant association with AR was also observed. In Model 4, participants classified as frail by FI had an increased risk of AR (OR = 1.587, 95% CI [1.116, 2.257]), indicating that the association was consistent across different frailty assessment methods (Figure 3).

Subgroup analysis of frailty status with AR

In exploratory subgroup analyses of variables with FDR-adjusted p < .05 in the multivariable regression models, frailty was positively associated with an increased risk of AR in certain subpopulations. Specifically, associations were observed in participants aged < 40 years (OR = 1.298, 95% CI [1.022, 1.648]) and 41–60 years (OR = 1.372, 95% CI [1.024, 1.838]), in males (OR = 1.516, 95% CI [1.134, 2.027]), individuals with a high school education (OR = 1.482, 95% CI [1.135, 1.934]), those with BMI ≥ 25 (OR = 1.823, 95% CI [1.388, 2.395]), white-collar workers (OR = 1.361, 95% CI [1.051, 1.761]), individuals with family income at 76–100% of the national median (OR = 1.433, 95% CI [1.072, 1.914]), those with a smoking history of ≥ 5 packs in their lifetime (OR = 1.265, 95% CI [1.040, 1.535]), those consuming alcohol ≥ 2 times per week (OR = 1.578, 95% CI [1.091, 2.282]), and nonasthmatic participants (OR = 1.256, 95% CI [1.057, 1.493]). These subgroup findings should be interpreted with caution, as they are exploratory in nature and not based on a priori power calculations (Table 2).

Table 2.

Subgroup analysis for the association between frailty status (assessed by FP) and allergic rhinitis with different clinical characteristics.

Allergic rhinitis OR [95% CI]	Age			Education level
Allergic rhinitis OR [95% CI]	<40 years old	41–60 years old	≥61 years old	Below high school	High school graduate	College or higher
Frailty status Yes vs no Model 4	1.298 (1.022, 1.648)*	1.372 (1.024, 1.838)*	1.030 (0.742, 1.431)	0.938 (0.687, 1.280)	1.482 (1.135, 1.934)†	1.265 (0.971, 1.647)
Allergic rhinitis OR [95% CI]	Sex		BMI		Smoking
Allergic rhinitis OR [95% CI]	Males	Females	<25	≥25	<5 pack in lifetime	≥5 pack in lifetime
Frailty status Yes vs no Model 4	1.516 (1.134, 2.027)†	1.099 (0.910, 1.327)	1.053 (0.862, 1.286)	1.823 (1.388, 2.395)‡	1.171 (0.860, 1.594)	1.265 (1.040, 1.535)*
Allergic rhinitis OR [95% CI]	Family income				Alcohol intake
Allergic rhinitis OR [95% CI]	0%–25%	26%–50%	51%–75%	76%–100%	<2 times/week	≥2 times/week
Frailty status Yes vs no Model4	1.166 (0.774, 1.756)	1.147 (0.841, 1.564)	1.259 (0.901, 1.759)	1.433 (1.072, 1.914)*	1.168 (0.976, 1.397)	1.578 (1.091, 2.282)*
Allergic rhinitis OR [95% CI]	Occupation			Asthma
Allergic rhinitis OR [95% CI]	White-collar jobs	Blue-collar jobs	Green-collar jobs	No	Yes
Frailty status Yes vs no Model 4	1.361 (1.051, 1.761)*	1.364 (0.879, 2.116)	1.238 (0.365, 4.202)	1.256 (1.057, 1.493)*	1.293 (0.608, 2.750)

Note: BMI: body mass index.

Model 4: adjusted for age, gender, BMI, education level, occupation, household income, residence, smoke, alcohol intake and asthma.

*p < .05; †p < .01, ‡ p < .001.

The association between AR and elements of frailty

Utilizing logistic regression analysis, the research further investigated the associations between AR and the components of frailty, including weight loss, slowness, weakness, emotional exhaustion, and low physical activity. Following adjustment for all relevant confounders (Model 4), a significant positive correlation between emotional exhaustion with AR (OR: 1.334, 95% CI [1.178, 1.510]) was observed (Table 3).

Table 3.

Multiple estimates for the association between allergic rhinitis and elements of frailty.

Components of frailty OR [95% CI]	Weight loss	weakness	Slowness	Exhaustion	Low physical activity
Allergic rhinitis	1.128 (0.967, 1.316)	0.956 (0.815, 1.121)	1.209 (0.990, 1.477)	1.334 (1.178, 1.510)‡	0.958 (0.756, 1.214)
(Yes vs no) Model 3	1.128 (0.967, 1.316)	0.956 (0.815, 1.121)	1.209 (0.990, 1.477)	1.334 (1.178, 1.510)‡	0.958 (0.756, 1.214)

Note: BMI: body mass index.

Model 4: adjusted for age, gender, BMI, education level, occupation, family income, residence, smoking, alcohol intake, and asthma.

*p < .05; † p < .01, ‡ p < .001.

Discussion

In this study, utilizing data from a nationwide cross-sectional survey, we identified a substantial association between frailty and the prevalence of AR, with this association particularly evident in males, younger and middle-aged adults, individuals with a high school education, those with BMI ≥ 25, white-collar workers, and those with higher family income, smoking history, and frequent alcohol consumption. These patterns may reflect underlying factors, such as emotional exhaustion, that co-occur with both frailty and AR, although the cross-sectional design precludes determination of causality. To our best knowledge, this is the first large-scale cross-sectional study to examine the correlation between frailty and the prevalence of AR.

Previous studies have consistently confirmed a close association between frailty and the prevalence and prognosis of various chronic respiratory diseases. Kolin et al.²⁵ have shown that the modified FI can predict the risk of requiring rescue medication following endoscopic sinus surgery, indicating that frailty assessment can serve as a reliable tool for predicting surgical risk. Goshtasbi et al.²⁶ found that a higher mFI score in patients undergoing sinus surgery was associated with an increased risk of complications, prolonged hospital stay, discharge to nonhome facilities, and mortality, further emphasizing the negative impact of frailty on airway diseases. Additionally, Park et al.²⁷ have indicated that frailty is associated with various chronic lower respiratory diseases, including ILD, asthma, COPD, and pleural diseases, highlighting the importance of identifying and managing frailty to improve patient outcomes and quality of life. Our study aligned with these findings and further demonstrated that frailty is independently associated with AR, highlighting its potential role in clinical management. Incorporating frailty assessment could help identify high-risk patients and guide personalized interventions, including lifestyle modifications, emotional support, and tailored pharmacologic strategies.

While the precise mechanisms linking frailty to AR remain to be fully elucidated, dysregulation of the body's inflammatory response in the context of frailty has been proposed as a potential contributor to increased susceptibility to allergic conditions.²⁸ Studies have indicated that frail individuals exhibit chronic low-grade inflammation, characterized by elevated levels of pro-inflammatory cytokines, including interleukin (IL)-1, IL-6, and IL-8. This inflammation impairs the integrity of nasal epithelial barriers and undermines ciliary clearance function, as a result facilitating the entry and prolonged retention of allergens, exacerbating the disease severity.^29,30 Additional studies revealed that frailty is associated with an increased oxidative stress response and aggravated cellular damage,³¹ which further contributes to the progression and severity of AR. Hence, the interplay of immune dysregulation, chronic systemic inflammation, and compromised tissue repair capacity in frail individuals collectively promotes the development and exacerbation of AR.

In exploratory subgroup analyses, frailty appeared to be associated with AR in specific demographic groups, including males, individuals aged ≤ 40 years or younger and 41–60 years, those with a high school education, those with BMI ≥ 25, and white-collar workers, findings that align with prior research. Hong et al.³² reported a higher prevalence of AR in men compared to women, reaching a peak at ages 20–29 and declining thereafter with increasing age. Despite the higher prevalence of frailty in the elderly, individuals aged > 60 years exhibit substantial heterogeneity due to factors such as multimorbidity and polypharmacy, which may confound the frailty–AR association.³³ In contrast, AR is more prevalent in younger and middle-aged individuals, among whom frailty may more accurately reflect underlying immune dysregulation and chronic low-grade inflammation, pathophysiological features shared by both conditions. Additionally, AR-related symptoms such as sleep disturbances, fatigue, and reduced physical activity may further exacerbate functional decline and promote the development of frailty in this demographic.³⁴ These findings underscore the importance of early identification of frailty in younger and middle-aged adults with chronic inflammatory diseases and highlight the need for further investigation into the bidirectional relationship between immune-mediated conditions and FPs in nonelderly populations. Ciprandi et al.³⁵ observed a significant increase in BMI among patients with AR compared to the control group, with overweight or obese patients exhibiting a greater number of abnormal functional and inflammatory parameters than those with normal weight. This suggests a link between obesity, low-grade systemic inflammation, and functional immune changes, potentially increasing the risk of AR. Park et al.³⁶ indicated that the prevalence of AR is higher among white-collar workers, which may be attributed to prolonged exposure to indoor environments where common allergens such as dust mites, pet dander, and molds are more easily encountered. Additionally, poor office ventilation may exacerbate exposure to these allergens. The high work-related stress among white-collar workers, along with psychological factors such as anxiety and depression, was closely associated with the occurrence and progression of AR.³⁷ These factors participated in the pathogenesis of AR via the neuro-immuno-endocrine network.³⁸ In our analysis, emotional exhaustion, a frailty component, was more evident in this occupational subgroup. These findings should be regarded as hypothesis-generating, as the subgroup analyses were not powered a priori. Larger prospective studies are needed to verify these patterns and elucidate possible bidirectional links between frailty and AR across diverse populations.

This study also has several limitations. First, the cross-sectional design of study prevented the determination of a causal relationship between AR and frailty. Second, the identification of AR relied on self-reported surveys, potentially introducing recall bias and outcome inaccuracies. Nevertheless, similar questionnaires have been used to diagnose AR in other large-scale population-based surveys.³⁹ Additionally, the modified FP and FI were operationalized using alternative indicators, as certain original components were not available in KNHANES. While these substitutions have been supported by prior research, they may still lead to potential classification errors and imperfect representation of the constructs typically measured through direct performance assessments. Furthermore, it is possible that frail individuals have more frequent interactions with healthcare providers compared to nonfrail peers, which may increase the likelihood of being diagnosed with AR. This potential detection bias is a common concern in database-based studies and may partially account for the observed associations. Although we adjusted for socioeconomic variables such as income and education, residual confounding due to healthcare utilization patterns cannot be ruled out. Finally, as this study was based on a sample of the Korean population, it may not be representative of other racial or geographical groups. The racial and geographical specificity of the sample may limit the generalizability of our findings to a broader population. Future research should employ more rigorous diagnostic methods, incorporating clinical symptoms, peripheral blood IgE levels, and skin prick tests, and should be conducted across diverse regional populations to enhance the universality of the results.

Conclusion

In summary, our findings suggest a potential association between frailty and the prevalence of AR, particularly in males, younger and middle-aged adults, individuals with a high school education, those with a BMI ≥ 25, and white-collar workers, and those with higher family income, smoking, and frequent alcohol consumption. Future longitudinal studies are warranted to address the limitations of the current research in order to elucidate the causal relationship between AR and frailty in greater detail. In addition, integrating standard AR treatments with frailty-targeted strategies may help improve symptom control, reduce recurrence, and enhance overall quality of life for affected individuals.

Supplemental Material

sj-docx-1-sci-10.1177_00368504251381587 - Supplemental material for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study

Supplemental material, sj-docx-1-sci-10.1177_00368504251381587 for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study by Lin Wang, Longgang Yu, Jisheng Zhang, Xudong Yan, Yan Jiang and Han Chen in Science Progress

Supplemental Material

sj-docx-2-sci-10.1177_00368504251381587 - Supplemental material for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study

Supplemental material, sj-docx-2-sci-10.1177_00368504251381587 for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study by Lin Wang, Longgang Yu, Jisheng Zhang, Xudong Yan, Yan Jiang and Han Chen in Science Progress

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sj-docx-3-sci-10.1177_00368504251381587 - Supplemental material for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study

Supplemental material, sj-docx-3-sci-10.1177_00368504251381587 for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study by Lin Wang, Longgang Yu, Jisheng Zhang, Xudong Yan, Yan Jiang and Han Chen in Science Progress

Supplemental Material

sj-docx-4-sci-10.1177_00368504251381587 - Supplemental material for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study

Supplemental material, sj-docx-4-sci-10.1177_00368504251381587 for Association between frailty and the risk of allergic rhinitis: Evidence from a nationwide cross-sectional study by Lin Wang, Longgang Yu, Jisheng Zhang, Xudong Yan, Yan Jiang and Han Chen in Science Progress

Footnotes

Acknowledgments

The authors thank E-tranStar (Beijing, China) for providing professional English language editing services.

ORCID iDs

Lin Wang

Longgang Yu

Jisheng Zhang

Xudong Yan

Han Chen

Ethics approval and consent to participate

The study received approval from the Institutional Review Board of the Korea Centers for Disease Control and Prevention (No. 2018-01-03-P-A). All KNHANES participants volunteered and provided informed consent.

Author contributions

Conceptualization: Lin Wang; data curation: Lin Wang; formal analysis: Lin Wang; funding acquisition: Yan Jiang; investigation: Lin Wang; methodology: Han Chen; project administration: Longgang Yu and Yan Jiang; resources: Jisheng Zhang; software: Jisheng Zhang; supervision: Han Chen; validation: Han Chen; visualization: Xudong Yan; writing—original draft: Lin Wang; writing—review and editing: Han Chen and Yan Jiang.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from National Natural Science Foundation of China (Grant No. 82471140), Key Technology Research and Development Program of Shandong Province (Competitive Innovation Platform Project) (Grant No. 2024CXPT054), Natural Science Foundation of Shandong Province (Grant No. ZR2023MH027), Natural Science Foundation of Qingdao Municipality (Grant No. 23-2-1-199-zyyd-jch), and Science and Technology Plan Project of Shinan District, Qingdao (Grant No. 2023-2-025-YY).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The datasets analyzed in this study are from the KNHANES 2016-2018, which are publicly available and can be downloaded from the KNHANES website:

Supplemental material

Supplemental material for this article is available online.

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