Abstract
Background:
Chronic rhinosinusitis is a common inflammatory condition that often leads to significant sleep disturbances, negatively affecting quality of life. Although intranasal steroids are a standard treatment, their effect on sleep disturbances in chronic rhinosinusitis has not been extensively studied.
Objective:
To determine whether a nasal steroid delivered through exhalation could improve sleep quality in chronic rhinosinusitis.
Methods:
Patients with chronic rhinosinusitis were treated with nasal fluticasone through an exhalation delivery system for 4 months. Sleep assessments were conducted at baseline and at 2 and 4 months post-treatment using comprehensive sleep and quality-of-life measures. Blood samples were taken at baseline to assess interleukin-6 levels. Regression analysis compared baseline and post-treatment data, adjusting for demographics. The influence of interleukin-6 levels and allergic comorbidities on treatment response was also evaluated.
Results:
Significant improvements were observed in sinonasal symptoms and sleep quality at 2 and 4 months (P <.05). Daytime sleepiness revealed trends toward improvement. Interleukin-6 levels below 6 ng/mL and absence of asthma were associated with response to treatment in terms of sleep quality.
Conclusion:
High-distribution nasal steroids delivered through an exhalation system significantly improved sleep quality in patients with chronic rhinosinusitis. However, certain subgroups, such as those with lower interleukin-6 and those without comorbid asthma, benefit more significantly in terms of sleep quality.
Introduction
Chronic rhinosinusitis (CRS) is a prevalent inflammatory condition affecting the paranasal sinuses, with an estimated prevalence exceeding 10% among adults in the United States.1 CRS is characterized by symptoms persisting for 12 weeks or longer, including mucopurulent drainage, nasal obstruction (congestion), facial pain-pressure-fullness, or a decreased sense of smell, alongside documented inflammation through nasal endoscopy or radiographic findings.2
The debilitating nature of CRS profoundly affects patients’ daily lives and overall quality of life. Comparative studies have revealed that patients with CRS experience significant limitations in social activities, which is comparable to individuals with other chronic conditions, such as congestive heart failure, chronic obstructive pulmonary disease, angina pectoris, and back pain/sciatica.3 One major contributor to the diminished quality of life in patients with CRS is sleep disturbance, with up to 75% of patients with CRS experiencing poor sleep.4 This leads to daytime fatigue, reduced cognitive performance, and impaired physical functioning.
A critical aspect that remains unclear is whether treatment for CRS can effectively improve sleep quality in this patient population. Intranasal steroids are a frequently used and accessible treatment option for CRS; however, traditional nasal sprays often fail to reach deep into the sinus cavities. In contrast, an exhalation delivery system for fluticasone allows for more efficient deposition of the medication into the paranasal sinuses,5 potentially enhancing its therapeutic effects. Thus, this study aims to investigate whether treatment with this high-distribution exhalation-delivery nasal steroid, which is already an approved treatment for CRS, can enhance sleep quality in these patients.6
The underlying mechanisms of the sleep dysfunction noted in CRS are not entirely clear, but sleep disturbances in other inflammatory conditions have been linked to elevated levels of the proinflammatory cytokine interleukin-6 (IL-6),7–9 which plays a role in regulating the sleep-wake cycle. In CRS, IL-6 is recognized as a significant contributor to inflammation.10–14 However, although IL-6 has been associated with poor sleep quality in other inflammatory diseases, its specific relationship with sleep disturbances in patients with CRS has not been thoroughly explored. This study aims to address this gap by investigating whether elevated IL-6 levels could serve as a biomarker for predicting response to treatment in terms of sleep improvement in patients with CRS.
In addition, the clinical application of endotyping patients with CRS—differentiating between various phenotypes and endotypes—may further elucidate the relationship between CRS, sleep disturbances, and treatment outcomes. Understanding these subtypes may enhance the personalization of treatment strategies.
Methods
Primary and Secondary Outcomes
The primary outcomes of this study were changes in sleep quality and sinonasal symptoms. The quality of sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI). Sinonasal symptoms were measured using the Sino-Nasal Outcome Test (SNOT-22). The primary end point was at 2 months of treatment with an exhalation-delivery nasal steroid. Changes were tracked from baseline to 4 months after treatment in a smaller subgroup.
Secondary outcomes included sleep dysfunction evaluated using the sleep domain of the SNOT-22 questionnaire (SNOT-Sleep), daytime sleepiness evaluated using the Functional Outcomes of Sleep Questionnaire (FOSQ-10) and the Epworth Sleepiness Scale, and sleep timing evaluated using the Munich Chronotype Questionnaire. Changes in these scores were measured at both the 2- and 4-month time points.
Systemic IL-6 levels were assessed at baseline to explore their relationship with sinonasal symptoms and sleep quality. This assessment provided insights into the potential role of IL-6 as a biomarker in this patient group.
Study Design
This institutional review board–approved prospective cohort study was conducted at a tertiary care center. Participants were recruited from the allergy/immunology and rhinology clinics and enrolled whether they had CRS. CRS was clinically diagnosed in accordance with the American Academy of Otolaryngology guidelines, having persistent rhinosinusitis symptoms for at least 12 weeks and with objective findings from computed tomography or endoscopic imaging.15 The inclusion criteria were selective to patients aged 18 to 75 years with CRS either with nasal polyp (CRSwNP) or without nasal polyp (CRSsNP), who were off antibiotic treatment for at least 3 weeks by the time of enrollment. Patients were excluded if their body mass index (BMI) was greater than 35 and if they had a known diagnosis of obstructive sleep apnea or sleep disorder. Patients were also excluded if they had any foreseen circumstances that would interfere with the treatment plan as identified by the prescribing physician. All patients were on standard intranasal steroids at the time of starting and were switched to the exhalation delivery device. All patients enrolled had not received oral corticosteroids for at least 1 month before and during the 2- to 4-month study period.
The study spanned 2 years for recruitment with each participant committing to a 2-month enrollment period. During the first month, patients were enrolled at a baseline visit, in which they completed a series of questionnaires and provided a blood specimen before starting the treatment. The first follow-up visit (follow-up #1) occurred between months 2 to 3 (8 weeks after starting the medication), during which participants completed the same questionnaires administered at the baseline visit. We had a subgroup that we followed with 4 months of treatment, which completed a second follow-up visit (follow-up #2) and completed the questionnaires again. Participants were provided with a 2-month supply of a deep exhalation-delivery system nasal fluticasone at the baseline visit, and the subgroup received the remaining 2-month supply at their follow-up #1. Blood samples were collected once for each participant at the baseline visit to assess IL-6 levels using sodium-heparin–treated tubes and analyzed using a high-sensitivity enzyme-linked immunosorbent assay (Meso Scale Diagnostics, LLC, V-PLEX PLUS proinflammatory panel 1 [human], catalog #K15049G) with a sensitivity of 0.06 pg/mL. We used the cutoff of 6 pg/mL to group patients to high and low IL-6, similar to previous studies that have used systemic IL-6 as a biomarker.16
Data Collection
All participants provided informed consent and completed the 5 questionnaires.
Comorbid conditions (eg, atopy, asthma, aspirin-exacerbated respiratory disease, gastroesophageal reflux disease, and autoimmune diseases) were assessed through patient questionnaires and electronic medical records. Diagnoses were confirmed by reviewing medical charts. Data were stored in a secure, institution-specific database through the use of REDCap (Reseach Electronic Data Capture, Rush University Medical Center), which serves as a web-based platform for research data management.17,18
Statistical Analysis
Analysis of variance and χ2 data analyses were initially conducted to report the demographics and characteristics of the study population and subgroups. A logistic regression analysis was done to assess the relationship between sleep quality (PSQI) and SNOT-22 outcomes and the categorical outcome variable representing the following 3 time points: baseline, 2 months post-treatment, and 4 months post-treatment while adjusting for age, sex, and BMI. The study population was divided into subgroups based on comorbidities, and subgroup analyses were conducted to test the impact of these comorbidities individually on response to treatment by logistic regression adjusting for age, sex, and BMI. A P value less than .05 was considered statistically significant. IBM SPSS Statistics version 29.0.0.0 was used to conduct our statistical analyses.
Results
Participant Characteristics
A total of 73 participants with CRS were recruited and enrolled in the study. All participants completed 2 months of deep delivery nasal corticosteroid treatment, and a subgroup of 33 of these participants completed the 4-month treatment period. The demographic characteristics of this cohort can be found in Table 1. The mean age, BMI, and duration of CRS of the cohort were 47.97 years (±15.20), 32.14 (±7.43), and 11.32 years (±10.27), respectively (Table 1). The cohort consisted of 68.5% females. Furthermore, 42% of the subjects had nasal polyposis.
Table 1.
Demographics and Clinical Characteristics in CRS Cohort
| Demographics and clinical characteristics | n (%) or mean (±SD) |
|---|---|
| Age | 47.97 (±15.20) |
| BMI | 32.14 (±7.43) |
| Sex | |
| Female | 50 (68.5) |
| Mean duration of CRS (y) | 11.32 (±10.27) |
| Polyp | 29 (40.3) |
| Asthma | 35 (48.6) |
| Atopy | 54 (75.0) |
| AERD | 3 (4.1) |
| OSA | 24 (33.3) |
Abbreviations: AERD, aspirin-exacerbated respiratory disease; BMI, body mass index; CRS, chronic rhinosinusitis; OSA, obstructive sleep apnea.
There were no statistically significant differences in age, sex distribution, and race between CRSsNP and CRSwNP. The mean age was 49.28 (±15.74) years for CRSsNP vs 47.72 (±13.88) years in patients with CRSwNP (P = .49). The sex distribution was 66.7% female among patients with CRSsNP compared with 69.3% female among patients with CRSwNP (P = .71). In addition, the race distribution between CRSwNP and CRSsNP was not significantly different (P = .19).
Primary Outcomes
Impact of Treatment on Sleep Quality
The primary end point for sleep quality demonstrated significant improvement. Sleep quality improved significantly as early as 2 months of treatment, with a mean PSQI score decrease from 9.41 (±3.94) at baseline to 8.07 (±4.04) (adjusted P=.035). This improvement was sustained through the 4-month primary end point, in which the mean PSQI score further decreased to 7.56 (±4.02) (adjusted P = .046) (Fig 1).
Figure 1.
Mean sinonasal and sleep outcome scores at baseline compared with 2-month and 4-month post-treatment. P values indicate level of significance (*P < .05, **P < .01, ***P < .001). FOSQ, Functional Outcomes of Sleep Questionnaire; Msfsc, mid-sleep on free days corrected for sleep debt (chronotype variable); PSQI, Pittsburgh Sleep Quality Index; SNOT, Sino-Nasal Outcome Test; SNOT-Sleep, Sino-Nasal Outcome Sleep Domain.
Impact of Treatment on Sinonasal Symptoms and Quality of Life
There were significant improvements in sinonasal symptoms and quality of life as measured by SNOT-22 (Table 2 and Fig 1). The mean SNOT-22 score decreased from 49.88 (±22.59) at baseline to 29.13 (±19.13) at 2 months (P<.001) and to 26.85 (±19.53) at 4 months after the treatment (P<.001).
Table 2.
Regression Comparing Mean Scores After 2 Months and 4 Months of Treatment With Deep Delivery Nasal Corticosteroid to Baseline Mean Scores
| Assessment | Baseline (ref) | 2 mo | 4 mo |
|---|---|---|---|
| Mean (±SD) | Mean (±SD) | Mean (±SD) | |
| PSQI | 9.41 (±3.94) | 8.07 (±4.04) | 7.56 (±4.02) |
| OR [95% CI]a | 0.91 [0.83-0.99] | 0.89 [0.79-1.00] | |
| P valueb | .035 | .046 | |
| SNOT-22 | 49.88 (±22.59) | 29.13 (±19.13) | 26.85 (±19.53) |
| OR [95% CI]a | 0.95 [0.93-0.97] | 0.94 [0.92-0.97] | |
| P valueb | <.001 | <.001 | |
| SNOT-sleep | 15.49 (±6.92) | 9.29 (±6.78) | 7.58 (±5.90) |
| OR [95% CI]a | 0.86 [0.81-0.91] | 0.83 [0.77-0.89] | |
| P valueb | <.001 | <.001 | |
| Msfsc | 1.69 (±2.07) | 2.25 (±2.12) | 1.36 (±2.09) |
| OR [95% CI]a | 1.17 [0.98-1.38] | 0.94 [0.75-1.18] | |
| P valueb | .076 | .58 | |
| Latency (weekday) | 38.44 (±34.50) | 31.32 (±26.76) | 30.24 (±26.43) |
| OR [95% CI]a | 0.99 [0.98-1.00] | 0.99 [0.98-1.01] | |
| P valueb | .16 | .26 | |
| Epworth | 8.56 (±5.40) | 7.41 (±4.39) | 6.62 (±4.36) |
| OR [95% CI]a | 0.95 [0.88-1.02] | 0.92 [0.84-1.01] | |
| P valueb | .14 | .079 | |
| FOSQ | 30.08 (±6.51) | 30.89 (±6.82) | 32.82 (±5.36) |
| OR [95% CI]a | 1.02 [0.97-1.08] | 1.07 [1.00-1.15] | |
| P valueb | .45 | .056 |
Abbreviations: FOSQ, Functional Outcomes of Sleep Questionnaire; Msfsc, mid-sleep on free days corrected for sleep debt (chronotype variable); OR, odds ratio; PSQI, Pittsburgh Sleep Quality Index; ref, reference; SNOT-22, Sino-Nasal Outcome Test; SNOT-Sleep, Sino-Nasal Outcome Sleep Domain.
Regression analysis was used to compare scores after 2 and 4 months of treatment with baseline scores in groups with and without comorbid conditions, adjusting for age, sex, and body mass index. The OR coefficient represents the likelihood of an event occurring at 2 months or 4 months compared with baseline.
Difference is significant at a level of P less than .05.
Secondary Outcomes
Impact of Treatment on Sleep Dysfunction and Daytime Sleepiness
Sleep dysfunction, as measured by the sleep component of the SNOT-22 assessment, demonstrated significant improvement from 15.49 (±6.92) at baseline to 9.29 (±6.78) at 2 months (P<.001) and 7.58 (±5.90) at 4 months (P<.001) of treatment (Table 2 and Fig 1).
Although there were no significant differences observed for daytime sleepiness, measured by Epworth or FOSQ scores between baseline and 2 months after treatment (P = .14 and P = .45, respectively), we observed a trend toward reduced daytime sleepiness for FOSQ and Epworth scores at 4 months after treatment (P = .056 and P = .079, respectively) (Fig 1). There were no significant differences in sleep timing, including latency between baseline and 2 months of treatment or 4 months of treatment (P = .16 and P = .26, respectively).
Subgroup Analysis
Comorbidity Impact on Sinonasal Symptoms and Sleep
Importantly, when assessing the cohort, having asthma affected everyday living and activity as measured by FOSQ at baseline (odds ratio [OR] = 1.12, 95% CI: 1.02-1.23, P = .016). In addition, having polyps revealed a trend toward poor SNOT-22 sinonasal symptoms (OR = 1.02, 95% CI: 0.996-1.05, P =.094).
Impact of comorbid conditions on response to therapy.
To assess the effect of comorbid conditions, we stratified the data and analyzed the subgroups based on the presence or absence of asthma, allergic rhinitis, and nasal polyps.
Asthma.
Sinonasal symptoms and SNOT-Sleep outcomes improved in both those with and without asthma after 2 and 4 months of treatment compared with baseline (Table 3). After 2 months of treatment, sleep quality improved in those without asthma but not in those with asthma. Furthermore, a trend toward significance was observed after 4 months only among the group without asthma (Table 3).
Table 3.
SNOT-22, SNOT-Sleep, and PSQI Stratified by Asthma, Allergic Rhinitis, and Nasal Polyps
| Time since treatment |
Time since treatment |
||||||
|---|---|---|---|---|---|---|---|
| Baseline | 2 mo | 4 mo | Baseline | 2 mo | 4 mo | ||
| Assessments | Without asthma | Asthma | |||||
|
| |||||||
| PSQI | Mean (±SD) | 9.24 (±4.16) | 7.39 (±3.29) | 6.93 (±3.05) | 9.57 (±3.81) | 8.77 (±4.63) | 7.89 (±4.70) |
| OR (95% CI)a | 0.843 (0.695-1.022) | 0.911 (0.781-1.063) | 0.942 (0.829-1.072) | 0.911 (0.781-1.063) | |||
| P valueb | .033 | .083 | .367 | .235 | |||
| SNOT-22 | Mean (±SD) | 45.27 (±21.35) | 24.92 (±18.29) | 20.69 (±13.23) | 53.14 (±21.70) | 33.57 (±19.25) | 31.89 (±22.20) |
| OR (95% CI)a | 0.944 (0.915-0.974) | 0.931 (0.889-0.975) | 0.945 (0.916-0.975) | 0.944 (0.912-0.978) | |||
| P valueb | <.001 | .002 | <.001 | .002 | |||
| SNOT-Sleep | Mean (±SD) | 14.65 (±7.48) | 7.97 (±6.40) | 5.54 (±4.29) | 16.11 (±6.20) | 10.69 (±6.98) | 9.21 (±6.52) |
| OR (95% CI)a | 0.858 (0.789-0.933) | 0.803 (0.703-0.917) | 0.844 (0.766-0.931) | 0.823 (0.735-0.921) | |||
| P valueb | <.001 | .001 | <.001 | <.001 | |||
| Without allergic rhinitis | Allergic rhinitis | ||||||
| PSQI | Mean (±SD) | 9.50 (±4.48) | 8.29 (3.93) | 8.00 (±4.12) | 9.37 (±3.83) | 8.00 (±4.11) | 7.29 (±4.10) |
| OR (95% CI)a | 0.920 (0.766-1.105) | 0.930 (0.744-1.164) | 0.895 (0.801-1.000) | 0.872 (0.755-1.007) | |||
| P valueb | .371 | .528 | .050 | .062 | |||
| SNOT-22 | Mean (±SD) | 45.56 (±21.63) | 30.67 (±21.81) | 35.00 (±21.35) | 50.28 (±21.84) | 28.61 (±18.35) | 24.35 (±18.54) |
| OR (95% CI)a | 0.957 (0.919-0.996) | 0.979 (0.935-1.024) | 0.934 (0.909-0.961) | 0.925 (0.893-0.959) | |||
| P valueb | .033 | .347 | <.001 | <.001 | |||
| SNOT-Sleep | Mean (±SD) | 15.00 (±7.43) | 8.39 (±7.09) | 8.33 (±6.84) | 15.48 (±6.76) | 9.59 (±6.71) | 7.48 (±5.69) |
| OR (95% CI)a | 0.833 (0.730-0.950) | 0.861 (0.742-0.998) | 0.842 (0.779-0.909) | 0.804(0.725-0.891) | |||
| P valueb | .006 | .047 | <.001 | <.001 | |||
| CRSsNP | CRSwNP | ||||||
| PSQI | Mean (±SD) | 9.67 (±4.03) | 8.19 (±4.10) | 7.69 (±4.70) | 9.00 (±3.91) | 7.90 (±4.02) | 7.29 (±3.48) |
| OR (95% CI)a | 0.887 (0.783-0.99) | 0.870 (0.732-0.99) | 0.926 (0.806-1.063) | 0.891 (0.753-1.056) | |||
| P valueb | .050 | .049 | .274 | .183 | |||
| SNOT-22 | Mean (±SD) | 52.72 (±19.74) | 32.56 (±19.15) | 29.93 (±18.04) | 43.72 (±23.74) | 24.03 (±18.25) | 25.06 (±21.21) |
| OR (95% CI)a | 0.936 (0.907-0.966) | 0.929 (0.891-0.968) | 0.954 (0.925-0.983) | 0.960 (0.929-0.992) | |||
| P valueb | <.001 | <.001 | .002 | .015 | |||
| SNOT-Sleep | Mean (±SD) | 16.49 (±6.50) | 10.21 (±6.71) | 7.80 (±6.45) | 13.69 (±7.20) | 7.93 (±6.77) | 7.65 (±5.63) |
| OR (95% CI)a | 0.832 (0.759-0.911) | 0.784 (0.692-0.888) | 0.876 (0.803-0.956) | 0.878 (0.794-0.970) | |||
| P valueb | <.001 | <.001 | .003 | .011 | |||
Abbreviations: CRSsNP, chronic rhinosinusitis without nasal polyps; CRSwNP, chronic rhinosinusitis with nasal polyps; OR, odds ratio; PSQI, Pittsburgh Sleep Quality Index; SNOT-22, Sino-Nasal Outcome Test; SNOT-Sleep, Sino-Nasal Outcome Test Sleep Domain.
Regression analysis was used to compare scores after 2 and 4 months of treatment with baseline scores in groups with and without comorbid conditions, adjusting for age, sex, and body mass index. The OR coefficient represents the likelihood of an event occurring at 2 months or 4 months compared with baseline.
Difference is significant at a level of P less than .05.
Allergic rhinitis.
Sinonasal symptoms and SNOT-Sleep outcomes improved in those with and without allergic rhinitis after 2 and 4 months of treatment (Table 3).
However, although having allergic rhinitis affected sleep quality improvement, a trend toward enhanced sleep quality was observed for those with allergic rhinitis after 2 and 4 months of treatment (Table 3). In contrast, there were no significant improvements in sleep quality after treatment in those without allergic rhinitis comorbidity (Table 3).
Nasal polyposis.
Sinonasal symptoms and SNOT-Sleep outcomes improved in both CRSwNP and CRSsNP after 2 and 4 months of treatment (Table 3). However, a trend toward significance for improved sleep quality was noted at 2 months of treatment only in patients without nasal polyposis. Conversely, sleep quality improvement was not observed for those with nasal polyps.
Interleukin-6 biomarker.
To investigate the role of systemic IL-6 as a potential biomarker, we divided the cases into those above and below 6 pg/mL. Our analysis found that participants with IL-6 levels less than 6 pg/mL had a strong and significant improvement in total SNOT-22 (OR = 0.941, 95% CI: 0.919-0.964, P < .001) and the SNOT-Sleep (OR = 0.843, 95% CI: 0.786-0.905, P < .001), which was not observed in those with higher levels. Furthermore, only those with low IL-6 experienced improved sleep quality measured by PSQI at 2 months (OR = 0.902, 95% CI: 0.814-0.999, P = .048).
Discussion
In summary, our investigation demonstrated that topical deep delivery of corticosteroids into the sinonasal cavity significantly improves sinonasal symptoms and sleep quality in patients with CRS. These improvements were evident regardless of the presence of asthma, allergic rhinitis, or nasal polyps, underscoring the profound impact of controlling inflammation in CRS and reinforcing the importance of addressing sinonasal inflammation in the management of this condition. Intranasal steroids, even in standard delivery forms, have been found to improve sleep in other upper respiratory system conditions.19,20 Our patients were already using standard delivery corticosteroids when they completed the initial surveys and then demonstrated improvement after switching to the exhalation device. However, a side-by-side study of standard intranasal and exhalation-delivery systems of the same corticosteroid may be needed to clearly demonstrate the incremental benefit of this treatment on sleep.
Although some improvements were noted, patients with elevated serum IL-6 levels, asthma, or nasal polyps exhibited less pronounced improvements in sleep quality compared with their counterparts. One possibility is that asthma, a lower airway disease, perpetuates inflammation beyond the sinonasal tract, limiting the full benefits of localized nasal steroid therapy.21 Persistent lower airway inflammation could explain why treating upper airway symptoms alone does not fully alleviate sleep disturbances.22 Similarly, nasal polyps often reflect more severe type 2 inflammation,23 which may resist standard steroid therapy and contribute to ongoing airway obstruction and discomfort during sleep. These results also highlight the need for a more tailored approach in those with systemic disease and involvement of both the upper and lower airways. Therefore, if the primary goal is to address sleep quality, patients with CRSwNP and comorbid asthma may benefit from additional therapies, such as biologics to effectively target systemic inflammation.24 Other factors, such as systemic inflammation, structural airway abnormalities, and preexisting sleep disorders, may also contribute to the variable responses to nasal steroid therapy in these groups. Further research is needed to elucidate these connections and explore the interplay of multiple factors.
Our study also indicates a role of IL-6 as a potential biomarker for predicting sleep-related responses in patients with CRS. Patients with IL-6 levels below 6 pg/mL responded well to exhalation-delivered nasal steroids, suggesting that IL-6 could serve as an objective predictor for treatment outcomes. In cases in which IL-6 levels are low, localized therapy may be sufficient for disease control. IL-6 and sleep disturbances have a bidirectional relationship, in which poor sleep can elevate IL-6 levels and elevated IL-6, in turn, contributes to sleep disruption.25 High IL-6 levels, indicative of systemic inflammation,26 are likely to limit the efficacy of topical therapies such as steroids in improving sleep quality. These patients may benefit more from systemic treatments, such as biologics, to address persistent sleep disturbances alongside sinonasal therapy. This finding aligns with the unified airway hypothesis, illustrating how inflammation across the respiratory system influences sleep quality. Moreover, our findings suggest that prolonged use of nasal steroid therapy may yield greater improvements in sleep-related quality of life. One explanation for the observed improved quality of sleep might be a reflection of improved sinusitis-related global quality of life and, in the case of CRS, improved respiratory inflammation. Although trends toward improved daytime functioning and reduced sleepiness were observed, these effects may become more pronounced with extended treatment durations or larger sample sizes. Of note, due to the specific nature of the survey questions used to evaluate sleep quality variables, some sleep improvements may not be reflected in these data. For example, the SNOT-Sleep primarily focuses on sinus-related sleep disturbances, whereas the PSQI provides a broader assessment of sleep quality. It is possible that certain improvements in sleep, such as reduced nighttime awakenings or enhanced sleep continuity, were not fully captured by these measures. Future studies using more comprehensive sleep assessments, including objective measures such as polysomnography or actigraphy, may provide a clearer understanding of the full impact of nasal steroid therapy on sleep quality. Future research should also investigate the long-term effects of nasal steroid therapy, focusing on whether longer courses of treatment or studies with larger populations could substantiate these preliminary findings.
This study has several limitations that warrant consideration. First, the absence of a control group comprising patients without CRS limits our ability to generalize findings beyond this specific population. However, the primary goal was to evaluate improvements in sleep quality, specifically among patients with CRS, rather than the general population. Furthermore, although IL-6 may help predict sleep-related responses to corticosteroids delivered through exhalation in CRS, the absence of a control group treated with standard intranasal steroids limits our conclusions. Nonetheless, the broader predictive potential of IL-6 with other treatment modalities warrants further investigation. In addition, although we controlled for common comorbidities, we did not explicitly screen for tobacco use, which could have confounded the results; larger studies should address this factor. Last, patients with obstructive sleep apnea were excluded from this study, restricting the applicability of our findings to this subgroup. Future studies should consider including this population to better understand the broader implications of nasal steroid therapy.
In conclusion, although this study provides important insights into CRS management, particularly in the context of sleep quality, it also emphasizes the need for personalized treatment strategies. The identification of IL-6 as a biomarker for predicting responses to nasal steroid therapy offers a promising tool for optimizing treatment outcomes. Larger, longer-term studies are needed to explore these trends further and solidify the role of biomarkers in enhancing both sleep quality and overall well-being in patients with CRS. Integrating these findings into the framework of the unified airway hypothesis underscores the complex interplay between upper and lower airway inflammation and its effects on sleep.
Funding
This project was funded by an unsolicited investigator-initiated grant from OptiNose. The study medications were provided for free to participating patients as part of this grant.
Footnotes
Disclosures
Dr Pete S. Batra serves on the advisory board for Neurent Medical, Inc. The remaining authors have no conflicts of interest to report.
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