Abstract
Background
Chronic spontaneous urticaria (CSU) is a common dermatological disorder impairing quality of life. The impact of CSU on sleep quality and obstructive sleep apnoea remains unclear.
Objective
To estimate the prevalence of sleep disturbance and obstructive sleep apnoea (OSA) in CSU patients and identify associated risk factors.
Methods
We conducted an analytical cross-sectional study involving adult CSU patients (n = 327) per eligibility criteria. We used the Pittsburgh Sleep Quality Index (PSQI) and STOP-Bang questionnaires to evaluate sleep disturbance and risk for OSA, respectively. Furthermore, we evaluated disease characteristics, including urticaria control and laboratory parameters, and assessed their association with sleep disturbance and risk for OSA.
Results
Poor sleep quality was reported by 59.3% CSU patients, while 61.5% presented with intermediate-to-high risk for OSA. PSQI score showed a positive correlation with mean platelet volume (p = 0.03), and negative correlations with age (p = 0.01), BMI (p = 0.04) and the Urticaria Control Test (UCT) score (p = 0.01). The STOP-Bang score showed a positive correlation with age (p < 0.001), BMI (p < 0.001), UCT score (p = 0.008) and PLR (p < 0.001), while the absolute eosinophil count was negatively correlated (p = 0.0006). Symptomatic dermographism (OR 2.4) significantly increased the odds of poor sleep, while uncontrolled urticaria (OR 0.5) and dyslipidemia (OR 0.4) reduced its odds. Female gender (OR 12.3), uncontrolled urticaria (OR 2.7) and dyslipidaemia (OR 3.1) were significant positive predictors for moderate-to-severe risk for OSA.
Conclusion
CSU patients frequently experience disturbed sleep and are at risk for developing OSA. Awareness about these comorbidities is important to enable their early identification and appropriate management.
Keywords: Chronic spontaneous urticaria, Sleep quality, Obstructive sleep apnoea, Pittsburgh sleep quality index, STOP-Bang questionnaire
Introduction
Chronic urticaria (CU) is an inflammatory skin disorder characterized by recurrent evanescent hives, angioedema, or both lasting over 6 weeks. It is classified as chronic spontaneous urticaria (CSU), with no identifiable triggers, and chronic inducible urticaria (CIndU), triggered by specific stimuli.1 CU affects 0.5–1% of the global population, with 66–93% being CSU.2 Mast cells play the central role in CSU pathogenesis, with histamine as the key mediator.1
CSU typically affects individuals aged 20–40 years and significantly impairs quality of life, mental health, and daily functioning.3 Patients frequently present with systemic comorbidities including respiratory, cardiovascular, gastrointestinal, neurologic, musculoskeletal, and sleep disorders. Sleep disturbances are increasingly recognized in CSU, especially nocturnal awakenings and difficulty in initiating sleep, contributing to fatigue, poor work performance, and economic burden.4,5 Although itching is a major cause, other contributors to disturbed sleep include impaired thermoregulation, skin barrier dysfunction, hormonal dysregulation, and cytokine involvement.6 Histamine, the key mediator of CSU, may exacerbate sleep issues due to its role in promoting wakefulness.7
Obstructive sleep apnoea (OSA), caused by repeated pharyngeal collapse during sleep, results in sleep deprivation and excessive daytime sleepiness.8 This collapse may be complete, resulting in apnoea or partial resulting in hypopnoea, subsequently disturbing respiratory gas exchange. It is associated with cardiovascular, metabolic, and neurocognitive complications. Recent studies by Perkowska et al.9 and Alatas et al.10 have reported OSA in 25–40% of CSU patients—significantly higher than the general population. Additionally, 4 in every 10 CU patients have moderate to high risk for developing OSA.11
Despite the considerable impact of CSU on sleep quality, data on sleep disturbances and OSA in CSU remain limited, especially in our setting. Furthermore, we need to identify potential risk factors for sleep disturbance and OSA in these patients to facilitate early diagnosis and targeted interventions, ultimately improving patients’ quality of life and reducing societal burden. This study aims to estimate the prevalence of sleep disturbances and OSA in CSU patients and identify associated risk factors.
Methods
Study design and patient selection
We conducted an institution-based, analytical cross-sectional study in the Department of Dermatology of a tertiary care center in India over 12 months (September 2023–August 2024) after obtaining Institutional Ethics Committee approval (CMSDH/IEC/08/02–2023). The study adhered to Good Clinical Practice guidelines, the Declaration of Helsinki, and Schedule Y guidelines.
We included CSU patients, diagnosed per current guidelines,1 aged 18–65 years of either gender, after obtaining written informed consent. Exclusion criteria were inducible, acute, or contact urticaria; hereditary angioedema; pruritic/allergic conditions (eg, food/drug allergy, collagen vascular disease, atopic dermatitis, autoimmune disorders, and malignancies with paraneoplastic pruritus, eg, Hodgkin's lymphoma); uncontrolled systemic illnesses (hepatic, renal, cardiovascular, respiratory, or thyroid disorders); patients with respiratory disorders including bronchial asthma and chronic obstructive pulmonary disease irrespective of disease control status to minimize confounding error; use of sedating medications (eg, antihistamines, antipsychotics) within the past 7 days; psychiatric or neurologic disorders; substance abuse; illiteracy or cognitive impairment; and pregnancy or lactation.
Sample size calculation and type of sampling
We calculated our sample size using formula N = z2pq/e2, where N is sample size, z = 1.96, p = prevalence in reference study, q= (1-p) and e (allowable error) = 0.05 (5%) and confidence interval 95%. Considering the prevalence of sleep disturbance to be 50% in CSU patients,5 our sample size translated to 384. Accordingly, 384 CSU patients were screened. Among them, 30 patients declined to provide written informed consent, 20 patients refused laboratory investigations, and 7 patients were unable to complete the Pittsburg Sleep Quality Index (PSQI) questionnaire correctly. Finally, 327 patients completed the study and were analyzed. (Fig. 1).
Fig. 1.
Flowchart of patient screening and enrolment. Note: Of 384 chronic spontaneous urticaria (CSU) patients screened from the departmental CSU registry by simple random sampling, 57 were excluded (30 declined written informed consent, 20 refused laboratory investigations, and 7 were unable to complete the Pittsburgh Sleep Quality Index [PSQI] questionnaire), leaving 327 patients included in the final analysis.
We recruited our patients by simple random sampling, using the computer-generated random number table and the departmental register of CSU patients as our sampling frame.
Evaluation of CSU patients
Eligible patients underwent detailed history-taking to document sociodemographic and disease characteristics (duration, presence of angioedema, symptomatic dermographism, and disease control via urticaria control test [UCT]). Controlled CSU was defined as UCT>12, while UCT<12 indicated uncontrolled CSU.1 All patients continued their regular non-sedating antihistamines. Those requiring sedating antihistamines were excluded per study criteria.
Sleep disturbance was evaluated using the Pittsburgh Sleep Quality Index (PSQI). The PSQI is a validated self-administered questionnaire assessing sleep quality over the previous month across 7 domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Each domain is scored from 0 to 3, generating a global score ranging from 0 to 21; scores ≥5 indicate poor sleep quality.9 (Table S1).
We used the validated STOP-Bang questionnaire to assess risk for developing obstructive sleep apnoea. The STOP-Bang is a validated screening tool for obstructive sleep apnoea consisting of 8 items: snoring, tiredness, observed apnoea, hypertension, BMI, age, neck circumference, and gender. Each item is marked as "Yes (1 point)” or "No (0 point)". Scores 0–2 indicate low risk, 3–4 intermediate risk, and 5–8 high risk for OSA.11,12 For analysis, patients were grouped into mild-risk and moderate-to-high-risk categories (Table S1).
We used the validated English versions of both PSQI and STOP-Bang questionnaires. The investigators explained any discrepancies in the native language whenever required to ensure proper understanding and facilitate accurate responses.
Blood investigations included absolute neutrophil count, absolute lymphocyte count, absolute eosinophil count, total platelet count, mean platelet volume (MPV), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), erythrocyte sedimentation rate (ESR), lipid profile, and total serum IgE. Associations of these parameters with sleep disturbance and OSA risk were analyzed.
After evaluation, all patients were treated according to standard guidelines. Patients with disturbed sleep and an increased risk of OSA have been referred to pulmonologists and are currently under follow-up.
Statistical analysis
Data were entered into Microsoft Excel and analyzed using MedCalc® v20. Descriptive statistics were expressed as mean ± SD for parametric data and median (IQR) for non-parametric data. Categorical variables were summarized using proportions. Data normality was assessed using the Shapiro-Wilk test. Group comparisons were performed using the t-test or chi-square test, as appropriate. Pearson correlation assessed associations between continuous variables. Multivariate logistic regression was used to identify risk factors for poor sleep and moderate-to-high risk of OSA. A p-value <0.05 was considered statistically significant.
Results
Study participants
We included 327 subjects with CSU (mean ± SD age 32.8 ± 11.8 years, 67% female) after satisfying the eligibility criteria. Almost half of our patients (48%, 157/327) were overweight, while 15.6% (51/327) were obese. The mean (SD) and median (IQR) duration of CSU were 12.2 (15.8) and 6 (3–12) months, respectively (range 1.5–72 months); 40.1% (131/327) of patients reported CSU for >6 months, while in the remaining 59.9%, the duration of CSU was 6 months or less. Concomitant angioedema and symptomatic dermographism were present in 41.6% (136/327) and 39.1% (128/327) of patients, respectively. The mean ± SD UCT score was 10.1 ± 2.6, and 199 patients (60.9%, n = 327) presented with uncontrolled urticaria.
Laboratory investigations revealed a raised eosinophil count (>500 cells/μL) in 8.3% (27/327) patients. The mean ± SD total platelet count was 277.2 ± 38.3 10^3/μL (normal value 150–450 10^3/μL), and the mean ± SD MPV was 8.9 ± 0.8 fL (normal value 7.5–11.5 fL). The mean ± SD NLR and PLR were 1.8 ± 0.3 (normal value 1–3) and 112.8 ± 19.1 (normal value 90–210), respectively. Most of our patients (78.3%, 256/327) presented with a raised total serum IgE level (>200 IU/ml), and the median (IQR) total serum IgE level was 305.2 (211.5–495.4) [mean ± SD 395.1 ± 275.1, range 85.2–1495.7).
Table 1 and Table 2 highlight the clinico-demographic and laboratory parameters of study participants.
Table 1.
Clinical-demographic characteristics of study participants.
| Characteristics | Total patients (n = 327) | Sleep quality |
Risk for OSA |
||||
|---|---|---|---|---|---|---|---|
| Good sleeper (n = 133) | Poor sleeper (n = 194) | p-value | Low risk (n = 126) | Intermediate to high risk (n = 201) | p-value | ||
| Age in years, mean (SD) | 32.8 (11.8) | 34.9 (13.4) | 31.3 (10.4) | 0.007∗ | 30.1 (8.9) | 34.4 (13.1) | 0.001∗ |
| Gender, n (%) | |||||||
| Male | 108 (33) | 41 (30.8) | 67 (34.5) | 0.6 | 9 (7.1) | 102 (50.7) | <0.0001∗ |
| Female | 219 (67) | 92 (69.2) | 127 (65.5) | 117 (92.9) | 99 (49.3) | ||
| BMI status, n (%) | |||||||
| Normal | 119 (36.4) | 43 (32.3) | 76 (39.2) | 0.3 | 62 (49.2) | 57 (28.4) | <0.0001∗ |
| Overweight | 157 (48) | 65 (48.9) | 92 (47.4) | 55 (43.6) | 102 (50.7) | ||
| Obesity | 51 (15.6) | 25 (18.8) | 26 (13.4) | 9 (7.1) | 42 (20.9) | ||
| Duration of CSU in months, mean (SD) | 12.2 (15.8) | 13.7 (17.8) | 11.1 (14.1) | 0.1 | 9.9 (13.1) | 13.6 (17.1) | 0.04∗ |
| Duration of CSU >6 months, n (%) | 131 (40.1) | 55 (41.4) | 76 39.2) |
0.8 | 41 (32.5) | 90 (44.8) | 0.04∗ |
| Angioedema, n (%) | 136 (41.6) | 56 (42.1) | 80 (41.2) | 0.9 | 54 (42.9) | 74 (36.8) | 0.5 |
| Symptomatic dermographism, n (%) | 128 (39.1) | 36 (27.1) | 92 (47.4) | 0.0003∗ | 48 (38.1) | 60 (29.9) | 0.0002∗ |
| UCT score, mean (SD) | 10.1 (2.6) | 10.6 (2.5) | 9.8 (2.6) | 0.009∗ | 9.7 (2.4) | 10.4 (2.6) | 0.03∗ |
| UCT category, n (%) | 0.0004∗ | <0.0001∗ | |||||
| Uncontrolled urticaria | 199 (60.9) | 65 (48.9) | 134 (69.1) | 95 (75.4) | 104 (51.7) | ||
| Well-controlled urticaria | 128 (39.1) | 68 (51.1) | 60 (30.9) | 31 (24.6) | 97 (48.2) | ||
| PSQI score, mean (SD) | 6.7 (2.4) | 4.4 (0.8) | 8.3 (1.8) | <0.0001∗ | 6.3 (2.4) | 6.9 (2.3) | 0.02∗ |
| PSQI sleep category, n (%) | |||||||
| Good sleeper | 133 (40.7) | – | – | – | – | – | – |
| Poor sleeper | 194 (59.3) | ||||||
| STOP-BANG score, mean (SD) | 2.8 (1.2) | 2.6 (1.3) | 3 (1.2) | 0.01∗ | 1.5 (0.7) | 3.7 (0.7) | <0.0001∗ |
| Risk for OSA, n (%) | |||||||
| Low risk | 126 (38.5) | – | – | – | – | – | – |
| Intermediate risk | 175 (53.5) | ||||||
| High risk | 26 (8) | ||||||
Abbreviations: OSA, Obstructive sleep apnoea; SD, Standard deviation; CSU, Chronic spontaneous urticaria; UCT, Urticaria control Test; PSQI, Pittsburgh Sleep Quality Index.
Table 2.
Laboratory parameters in study participants.
| Characteristics | Total patients (n = 327) | Sleep quality |
Risk for OSA |
||||
|---|---|---|---|---|---|---|---|
| Good sleeper (n = 133) | Poor sleeper (n = 194) | p-value | Low risk (n = 126) | Intermediate to high risk (n = 201) | p-value | ||
| Absolute neutrophil count (10^3/μL), mean (SD) | 4.5 (0.7) | 4.4 (0.7) | 4.5 (0.7) | 0.4 | 4.5 (0.7) | 4.4 (0.6) | 0.4 |
| Absolute lymphocyte count (10^3/μL), mean (SD) | 2.5 (0.2) | 2.4 (0.2) | 2.5 (0.1) | 0.3 | 2.5 (0.2) | 2.4 (0.1) | 0.3 |
| Absolute eosinophil count (/uL), mean (SD) | 256 (147.6) | 239 (130.1) | 267.1 (157.9) | 0.09 | 287.2 (153.8) | 236.5 (140.5) | 0.002∗ |
| Raised eosinophil count (>500/μL), n (%) | 27 (8.3) | 9 (6.7) | 18 (9.3) | 0.5 | 17 (13.5) | 10 (4.9) | 0.02∗ |
| Total platelet count (10^3/μL), mean (SD) | 277.2 (38.3) | 274.2 (41.2) | 279.2 (36.1) | 0.2 | 266.5 (37.6) | 283.9 (37.3) | 0.0001∗ |
| Mean platelet volume (fL), mean (SD) | 8.9 (0.8) | 8.7 (0.8) | 9.1 (0.8) | <0.0001∗ | 8.8 (0.8) | 8.9 (0.8) | 0.05 |
| Neutrophil-lymphocyte ratio (NLR), mean (SD) | 1.8 (0.3) | 1.8 (0.3) | 1.8 (0.2) | 0.9 | 1.8 (0.3) | 1.8 (0.2) | 0.6 |
| Platelet-lymphocyte ratio (PLR), mean (SD) | 112.8 (19.1) | 112.4 (21.3) | 113.1 (17.5) | 0.8 | 107.9 (18.6) | 115.9 (18.8) | 0.0002∗ |
| Raised erythrocyte sedimentation rate (ESR), n (%) | 313 (95.7) | 133 (100) | 180 (92.8) | 0.004∗ | 124 (98.4) | 189 (94) | 0.1 |
| Dyslipidemia, n (%) | 85 (26) | 47 (35.3) | 38 (19.6) | 0.002∗ | 18 (14.3) | 67 (33.3) | 0.0002∗ |
| Serum total IgE (IU/ml), mean (SD) | 395.1 (275.1) | 386.3 (317.9) | 401.1 (242.2) | 0.6 | 428.3 (327.3) | 374.2 (235.2) | 0.08 |
| Raised total serum IgE (>200 IU/ml), n (%) | 256 (78.3) | 98 (73.7) | 158 (81.4) | 0.1 | 102 (80.9) | 154 (76.6) | 0.4 |
Abbreviations: OSA, Obstructive sleep apnoea; SD, Standard deviation; NLR, Neutrophil-lymphocyte ratio; PLR, Platelet-lymphocyte ratio; ESR, Eryhrocyte sedimentation rate
Sleep quality and associated factors
Six in every 10 CSU patients (59.3%, 194/327) reported poor sleep quality (PSQI score> 5). The mean (SD) PSQI score was 6.7 (2.4) in our study subjects.
Symptomatic dermographism (p = 0.0003) and uncontrolled urticaria (p = 0.0004) were significantly more frequent in poor sleepers compared to good sleepers. The mean (SD) STOP-Bang score was also higher in poor sleepers (p = 0.01), suggesting increased risk for OSA. On the contrary, mean (SD) age and mean (SD) UCT score were higher [suggestive of well-controlled urticaria] in good sleepers (p's 0.007 and 0.009, respectively) (Table 1). Laboratory investigations revealed significantly higher MPV (p < 0.0001) and ESR (p = 0.004) in poor sleepers, while dyslipidemia (p = 0.002) was more frequent in good sleepers (Table 2).
We found a significant positive correlation between total PSQI score and MPV (rp = 0.2, p = 0.03), while PSQI score showed a significant negative correlation with age (rp = − 0.1, p = 0.01), BMI (rp = − 0.1, p = 0.04) and UCT score (rp = − 0.1, p = 0.01). NLR showed a negative correlation (rp = − 0.008, p = 0.9), while PLR showed a positive correlation (rp = 0.06, p = 0.3) with total PSQI score.
Univariate regression analysis revealed that uncontrolled urticaria (OR 1.4, 95% CI 1.3–1.7) and symptomatic dermographism (OR 2.4, 95% CI 1.5–3.9) were significant positive predictors of poor sleep quality, while age <30 years (OR 0.6, 95% CI 0.4–0.9) and dyslipidemia (OR 0.4, 95% CI 0.3–0.7) were significant negative predictors. Other positive predictors of poor sleep included female gender (OR 1.2 95% CI 0.7–1.9) and raised total serum IgE (OR 1.6 95% CI 0.9–2.7). However, on multivariate logistic regression, we found that only uncontrolled urticaria (OR 1.5 95% CI 1.3–1.8) and symptomatic dermographism (OR 2.4, 95% CI 1.4–4) significantly increased the odds of poor sleep [Table 3].
Table 3.
Risk-factors associated with ‘poor sleep quality’ and ‘moderate-to-high risk of obstructive sleep apnoea’ in CSU patients by regression analysis.
| Parameters | Poor sleep quality (PSQI >5) |
Moderate-to-high risk for OSA |
||
|---|---|---|---|---|
| Univariate regression OR (95% CI) | Multivariate regression OR (95% CI) | Univariate regression OR (95% CI) | Multivariate regression OR (95% CI) | |
| Age <30 years | 0.6 (0.4–0.9)∗ | 0.7 (0.5–1.2) | 1.3 (0.8–2) | - |
| Female gender | 1.2 (0.7–1.9) | - | 12.6 (6.1–26.2)∗ | 12.3 (5.7–26.8)∗ |
| Duration >6 months | 0.9 (0.6–1.4) | - | 1.7 (1.1–2.7)∗ | 1.2 (0.7–2.1) |
| Overweight and obesity | 0.7 (0.5–1.2) | - | 2.4 (1.5–3.9)∗ | 1.4 (0.8–2.4) |
| Angioedema | 0.9 (0.6–1.5) | - | 0.9 (0.6–1.4) | - |
| Symptomatic dermographism | 2.4 (1.5–3.9)∗ | 2.4 (1.4–4)∗ | 1.1 (0.7–1.7) | - |
| Uncontrolled urticaria (UCT <12) | 1.4 (0.3–0.7)∗ | 1.5 (0.3–0.8)∗ | 2.9 (1.7–4.7)∗ | 2.7 (1.6–4.8)∗ |
| Dyslipidemia | 0.4 (0.3–0.7)∗ | 0.4 (0.3–0.8)∗ | 3 (1.7–5.3)∗ | 3.1 (1.6–6.3)∗ |
| Diabetes mellitus | 0.7 (0.3–1.7) | - | 1.1 (0.5–1.5) | - |
| Raised serum IgE | 1.6 (0.9–2.7) | - | 0.7 (0.4–1.3) | - |
∗p < 0.05, Abbreviations: CSU, Chronic spontaneous urticaria; UCT, urticaria control test; PSQI, Pittsburg sleep quality index; OSA, obstructive sleep apnoea; OR, odds ratio; CI, confidence interval.
The proportion of patients according to individual PSQI components and urticaria control (ie, uncontrolled and well-controlled) is summarized in Table 4 and Fig. 2.
Table 4.
Relationship between PSQI sleep components and urticaria control (n = 327).
| Name of component | Grade | Uncontrolled urticaria (n) | Well-controlled urticaria (n) |
|---|---|---|---|
| PSQI subjective sleeping quality p = 0.002∗ | 0 | 43 | 43 |
| 1 | 52 | 49 | |
| 2 | 60 | 10 | |
| 3 | 44 | 26 | |
| PSQI sleep onset latency; p = 0.6 | 0 | 19 | 13 |
| 1 | 63 | 37 | |
| 2 | 86 | 53 | |
| 3 | 31 | 25 | |
| PSQI sleep duration; p = 0.0002∗ | 0 | 77 | 73 |
| 1 | 73 | 41 | |
| 2 | 48 | 14 | |
| 3 | 1 | 0 | |
| PSQI sleep efficiency; p = 0.6 | 0 | 171 | 113 |
| 1 | 24 | 11 | |
| 2 | 1 | 4 | |
| 3 | 3 | 0 | |
| PSQI sleep difficulty; p = 0.9 | 0 | 2 | 0 |
| 1 | 125 | 82 | |
| 2 | 72 | 46 | |
| PSQI daytime dysfunction; p = 0.3 | 0 | 187 | 123 |
| 1 | 6 | 3 | |
| 2 | 3 | 2 | |
| 3 | 3 | 0 | |
| PSQI sleep medication; p = 0.4 | 0 | 57 | 51 |
| 1 | 62 | 21 | |
| 2 | 34 | 31 | |
| 3 | 46 | 25 |
Abbreviations: PSQI, Pittsburg sleep quality index.
Fig. 2.
Distribution of patients across individual Pittsburgh Sleep Quality Index (PSQI) component categories, stratified by urticaria control status (uncontrolled vs. well-controlled CSU). Note: P-values denote between-group comparisons for each PSQI component; sleep duration (p = 0.0002) and subjective sleep quality (p = 0.002) differed significantly between uncontrolled and well-controlled urticaria, while sleep medication use, daytime dysfunction, sleep difficulty, sleep efficiency, and sleep onset latency did not.
Risk for OSA and associated factors
In our study, almost half of the patients (53.5%, 175/327) presented with an intermediate risk for OSA, 8% (26/327) had a high risk, while the remaining 38.5% (126/327) had a low risk of developing moderate-to-severe OSA. The mean (SD) STOP-Bang score was 2.8 (1.2) in our study participants.
Higher mean age, female gender, overweight and obesity, prolonged duration of CSU (>6 months), symptomatic dermographism, higher mean PSQI score, lower absolute eosinophil count, higher total platelet count, higher platelet-lymphocyte ratio (PLR) and dyslipidemia were significantly associated with intermediate to higher risk for OSA. Paradoxically, subjects with intermediate to high risk for OSA showed a significantly higher prevalence of well-controlled urticaria (p < 0.0001) corroborated by a higher mean UCT score (p = 0.03). (Tables 1 and 2).
A significant positive correlation was found between total STOP-Bang score and age (rp = 0.4, p < 0.001), BMI (rp = 0.3, p < 0.001), UCT score (rp = 0.1, p = 0.008) and PLR (rp = 0.3, p < 0.001), while AEC showed a significant negative correlation (rp = − 0.2, p = 0.0006). NLR showed a negative correlation with STOP-Bang score, but it was not statistically significant (rp = − 0.04, p = 0.4).
On univariate analysis, we detected multiple significantly positive predictors for intermediate/moderate to high risk for OSA, such as female gender (OR 12.6, 95% 6.1–26.2), duration of CSU >6 months (OR 1.7 95% CI 1.1–2.7), overweight and obesity (OR 2.4, 95% CI 1.5–3.9), uncontrolled urticaria (OR 2.9, 95% CI 1.7–4.7) and dyslipidaemia (OR 3.0, 95% CI 1.7–5.3). Symptomatic dermographism (OR 1.1, 95% CI 0.7–1.7) and diabetes mellitus (OR 1.1, 95% CI 0.5–1.5) also showed a positive association but not statistically significant. On logistic regression, we found female gender (OR 12.3, 95% CI 5.7–26.8), uncontrolled urticaria (OR 2.7, 95% CI 1.6–4.8) and dyslipidemia (OR 3.1, 95% CI 1.6–6.3) to be significant positive predictors for moderate-to-severe risk for OSA (Table 3).
The proportion of patients in each STOP-Bang category with CSU that is poorly or well-controlled is depicted in Fig. 3.
Fig. 3.
Distribution of patients according to individual STOP-Bang questionnaire components and stratified by urticaria control status (uncontrolled vs. well-controlled CSU).
Discussion
The association between chronic spontaneous urticaria (CSU) and sleep disturbances, including obstructive sleep apnoea (OSA), is an emerging area of research. CSU patients often experience itching-related insomnia, which impairs sleep initiation and maintenance.13 Additionally, recent studies suggest that CSU patients may be more susceptible to sleep-related breathing disorders, particularly OSA, compared to the general population.9,10,14 However, there is limited data in the present regional context, which this study aimed to address.
In our study (n = 327), 59.3% of CSU patients reported poor sleep quality (PSQI >5), with a mean PSQI score of 6.7 ± 2.4. This aligns with previous findings by Kaaz et al (48.5%, median PSQI 4.5, n = 64),15 Sanchez-Diaz et al (78.6%, mean PSQI 9.5, n = 75),16 and Ates et al (87.5%, n = 21) in CSU patients.17 Notably, an Indian study reported sleep disturbances in 36.3% of healthy individuals, which is considerably lower than the 59.3% observed in our CSU patients, thus supporting the higher burden of sleep disturbance in this population.18 Differences in prevalence may be attributed to sample heterogeneity and socio-demographic variation. Sleep impairment in CSU is comparable to other chronic dermatoses such as atopic dermatitis and bullous pemphigoid.19
We identified symptomatic dermographism and uncontrolled urticaria (UCT <12) to be significant positive predictors of poor sleep, which may be attributed to increased disease burden and greater pruritus severity during night. Poor sleepers had significantly worse urticaria control (p = 0.009), consistent with Sanchez-Diaz et al16 While prior studies associated sleep impairment with disease activity (UAS7),15 we evaluated control using UCT. Can et al20 reported positive correlations between PSQI and eosinophil count/CRP, while we found a positive correlation with MPV (p = 0.03) and negative correlations with age (p = 0.01), BMI (p = 0.04), and UCT score (p = 0.01). The mean platelet volume (MPV) reflects the average size of platelets in circulation and is a sensitive marker for platelet activation.21 Increased MPV, indicating larger and more reactive circulating platelets that release pro-inflammatory mediators such as IL-1α, IL-1β, and TGF-β, along with endothelial dysfunction and heightened sympathetic activity due to the release of immunomodulatory neurotransmitters stored within platelet granules, has been reported in several inflammatory disorders, including chronic urticaria.22,23 This pro-inflammatory and sympathetic mileu may independently impair sleep quality, underlying the positive correlation between MPV and PSQI score in our subjects. The weak inverse correlation between age or BMI and PSQI score should be interpreted with caution. Younger patients may experience a greater psychosocial impact of CSU, while the BMI association may be attributed to demographic heterogeneity and confounding effects related to metabolic status or disease perception and needs further corroboration in longitudinal studies. The negative correlation between UCT score and PSQI score is biologically plausible and suggests that better disease control (higher UCT score) is associated with improved sleep quality, possibly due to reduced nocturnal itching and psychological stress.
Although elevated serum IgE indicates Type I autoimmune endotype of CSU, its association with sleep disturbance in CSU remains unclear.24 In our study, raised IgE levels showed only a weak, non-significant relationship with PSQI scores.
Regarding OSA, 53.5% of our patients were at intermediate risk and 8% at high risk per STOP-Bang scores (mean 2.8). In comparison, Cherrez-Ojeda et al11 reported 24% intermediate and 20.5% high risk, with a mean score of 2.5. Several other authors have also reported a higher frequency of sleep-related breathing disorders and OSA in CSU patients compared to the general population.9
Although we used a validated screening tool, resource constraints limited confirmatory polysomnography. Notably, Alatas et al reported confirmed OSA in 41.9% of CSU patients by polysomnography.10 Thus, our findings are consistent with the existing literature that CSU patients are more predisposed to develop OSA, similar to other allergic disorders like bronchial asthma and allergic rhinitis.25 These authors also emphasize the screening for OSA in CSU patients for optimizing their therapeutic outcome.
We found significant positive correlations between STOP-Bang scores and age (p < 0.001), BMI (p < 0.001), UCT (p = 0.008), and PLR (p < 0.001), in agreement with findings by Hendy et al26 and Alatas et al,10 while eosinophil count showed a negative correlation (p = 0.0006). The positive correlation between STOP-Bang score and BMI may be attributed to increased adipose tissue deposition around the upper airway resulting in pharyngeal narrowing, increased airway collapsibility, and impaired respiratory drive during sleep.27 Increased age also results in the weakening of the respiratory muscles resulting in impaired breathing during sleep, while a raised PLR is an indicator of underlying systemic inflammation and endothelial dysfunction, which are being increasingly associated with disturbed sleep.22 Overweight or obesity and prolonged disease duration further aggravate the CSU-related inflammatory state, raising the OSA risk. The negative correlation with eosinophil count may be attributed to higher disease activity, autoimmunity, and therapeutic refractoriness associated with eosinopenia.28 The positive correlation with UCT score, ie, well-controlled urticaria, is difficult to interpret, and may have occurred due to confounding factors that could not be addressed, like disease perception or improper sleep hygiene. On logistic regression, uncontrolled urticaria (OR 2.7, 95% CI 1.6–4.8) and dyslipidemia (OR 3.1, 95% CI 1.6–6.3) were significant positive predictors for moderate-to-severe risk for OSA, similar to previous studies.11,29 We also detected female gender (OR 12.3, 95% CI 5.7–26.8) to be a significant risk factor for OSA, while another study has reported male gender to be associated with a higher risk of developing OSA.30 Our results also revealed associations between OSA risk and metabolic disorders such as obesity and diabetes, consistent with previous reviews linking dermatologic conditions, OSA, and metabolic dysfunction.31 Although several studies link OSA with disease severity and reduced quality of life in CSU, we did not evaluate QoL directly.11,32
Laboratory parameters such as eosinophil count, total IgE, NLR, PLR, and MPV should not be considered as independent determinants of sleep impairment or OSA risk without the background of CSU. CSU is being increasingly recognized as a systemic inflammatory disorder, and these biomarkers, such as MPV, PLR, eosinophil count, and serum IgE may reflect different aspects of inflammatory activity, disease endotypes, or associated metabolic dysfunction.33 Thus, their relationship with sleep quality and OSA risk in CSU patients is likely indirect and should be interpreted in conjunction with clinical indicators such as UCT score, disease duration, and symptomatic dermographism to gain a deeper understanding of sleep impairment in CSU patients.
Notably, recent population-based evidence suggests that pre-existing OSA may predispose to CSU onset, though the direction of causality remains unclear.14 Future prospective studies using objective sleep assessments and comprehensive outcome measures are needed to better define this relationship.
Strengths and limitations
The study's major strength is its relatively large sample size (n = 327), possibly the highest in a similar study till date. It is also the first study from India evaluating sleep quality and OSA risk in CSU patients. Key limitations include the absence of a control group, a cross-sectional design limiting causal inference, and the unavailability of confirmatory polysomnography due to resource constraints. Sedating antihistamines can act as an important confounding factor by independently altering sleep quality, and we attempted to minimize this by excluding patients who had taken such medications within the previous week. Additionally, CU-QoL was not assessed, as it is a relatively extensive questionnaire and could not be feasibly administered within the study setting. Furthermore, disease control and symptom severity in CSU are influenced by treatment status and disease duration, which may interfere with the sleep quality and OSA risk.
Conclusion
CSU patients commonly experience poor sleep quality and are at increased risk of OSA. Clinicians should recognize sleep disturbance as an important comorbidity and screen high-risk patients—especially those with uncontrolled urticaria, symptomatic dermographism, dyslipidemia, or the female gender. Early identification and appropriate referral for sleep evaluation may improve overall management and quality of life.
Authors' consent for publication
All authors have read and approved the final version of the manuscript and consent to its publication.
Authors contribution
Indrashis Podder: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Validation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration.
Siddhartha Nath: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Validation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration.
Subhajit Sadhukhan: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Validation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration.
Karla Robles-Velasco: Methodology, Validation, Writing – Original Draft, Writing –Review & Editing, Visualization.
Ivan Cherrez-Ojeda: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Validation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available due to privacy and institutional restrictions but are available from the corresponding author upon reasonable request.
Ethics approval
Institutional Ethics Committee approval (CMSDH/IEC/08/02-2023). The study obeyed good clinical practice guidelines, the Declaration of Helsinki, Schedule and guidelines.
Declaration of competing interest
None.
Acknowledgement and funding
We acknowledge the Indian Council of Medical Research for funding this project as a part of the ICMR STS Program (Reference ID: 2023-00343).
Footnotes
Full list of author information is available at the end of the article
Supplementary data to this article can be found online at https://doi.org/10.1016/j.waojou.2026.101464.
Appendix A. Supplementary data
The following is the Supplementary data to this article.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to privacy and institutional restrictions but are available from the corresponding author upon reasonable request.