This study was designed as a cross-sectional, case–control investigation intended to compare circadian rhythm protein levels in children diagnosed with asthma and in healthy controls.

This study was approved by the Health Sciences University Hamidiye Scientific Research Ethics Committee (Date: November 14, 2024; No: 2024/13-13/2). Written informed consent was obtained from all participants and their legal guardians.

A post-hoc power analysis based on the observed effect size for BMAL1 (Cohen’s d = 0.87) was conducted using GPower software, indicating that the current sample of 43 children with asthma and 34 healthy controls achieved a power of approximately 89% at an alpha level of 0.05.

Bovine serum albumin (BSA), Folin-Ciocalteu’s phenol reagent, potassium phosphate monobasic (KH₂PO₄), and sodium potassium tartrate (NaK tartrate) were purchased from Sigma (St. Louis, MO, USA). Copper sulfate (CuSO₄), sodium hydroxide (NaOH), sodium carbonate anhydrous (Na₂CO₃), and sodium phosphate dibasic dihydrate (Na₂HPO₄•2H₂O) were obtained from Riedel-de Haën (Germany). Human CRY1 and CRY2 ELISA kits were obtained from Elabscience (USA), Human PER1, PER2, and BMAL1 ELISA kits from BT LAB (UK), and the Human CLOCK ELISA kit from MyBioSource (USA).

Children between the ages of 8 and 17 who had been followed in a pediatric allergy clinic for at least three visits, who had not experienced an acute asthma exacerbation or used rescue medication in the previous month, and who satisfied the Global Initiative for Asthma (GINA) diagnostic criteria were included in the asthma group.¹ Asthma diagnosis was confirmed by evaluating clinical symptoms—namely, wheezing, shortness of breath, chest tightness, or cough that worsened at night or early in the morning—and by demonstrating an increase in FEV₁ of at least 12% and 200 mL after bronchodilator administration. Additional tests, such as exercise challenges or bronchial provocation, were conducted if necessary. Exclusion criteria included any neurologic, cardiac, metabolic, or psychiatric conditions; acute infection within the previous week; inability to perform spirometry; and the presence of comorbid atopic diseases such as allergic rhinitis, atopic dermatitis, or food allergy. The healthy control group consisted of children of similar age and sex distribution who did not have asthma or other chronic systemic diseases and had no significant comorbidities. A total of 43 children with asthma and 34 healthy controls were enrolled.

Pulmonary function tests were performed in accordance with the American Thoracic Society and European Respiratory Society guidelines using a calibrated spirometer.²¹ Participants were seated in a well-ventilated room and instructed to take a deep breath and exhale forcefully into the spirometer for as long and as hard as possible. Each participant performed at least three acceptable forced vital capacity (FVC) maneuvers, and the best measurements were recorded for analysis. Parameters included forced expiratory volume in one second (FEV₁), the FEV₁/FVC ratio, peak expiratory flow (PEF), and mid-expiratory flow (MEF_25–75). All tests were supervised by a trained technician to ensure quality control and reproducibility.

Asthma control was evaluated using the Asthma Control Test (ACT), which has been validated for use in the Turkish population.²² The ACT consists of five questions that assess symptoms and the use of reliever medication over the preceding four weeks, yielding a score of 20–25 for well-controlled asthma, 15–19 for partially controlled asthma, and 5–14 for uncontrolled asthma. The questionnaire was administered to all participants on the same day as pulmonary function testing.

Sleep quality was measured using the Children’s Sleep Habits Questionnaire (CSHQ), a parent-reported instrument that examines key aspects of sleep, including bedtime resistance, nighttime awakenings, breathing problems during sleep, and morning wakefulness.²³ A total score of 41 or higher was considered indicative of poor sleep quality, whereas a score below 41 indicated good sleep quality.

Blood samples for circadian rhythm protein analysis were collected from all participants in the morning (08:00–10:00), preferably under fasting conditions. Because many inflammatory mediators and clock-regulated transcripts exhibit diurnal variation, a fixed morning window was used to limit time-of-day confounding and standardize pre-analytical conditions in this pediatric cohort.^24,25 Samples were centrifuged at 4 °C (10 min, 4500 rpm), serum was aliquoted, and stored at −80 °C for up to three months.²⁶ The Lowry method for protein determination was used to measure the total protein content of the specimens.²⁷ Total protein content was used to calculate the serum levels of circadian rhythm proteins. Serum concentrations of clock proteins (BMAL1, CLOCK, CRY1, CRY2, PER1, PER2) were measured by sandwich ELISA (per the manufacturer’s instructions) using a 7-point calibration fitted with a 4-parameter logistic model. All samples and standards were run in duplicate; plates included a blank, a matrix-matched control, and two QC controls (low/high). Duplicate variability >15% CV triggered re-measurement. Inter- and intra-assay CVs across QC levels were <10% and <12%, respectively.

Total protein in each serum sample was quantified in duplicate by the Lowry method using bovine serum albumin standards. To reduce matrix-related variability, ELISA concentrations (after applying the appropriate dilution factor) were normalized to the sample’s total protein and reported as pg per mg total protein (pg/mg)—that is, the ELISA-derived concentration per unit of total protein in the same sample. Values below the lower limit of quantification (LLOQ) were treated as missing and excluded from statistical analyses. Plate performance was monitored using QC controls, and no plate-wise correction was required.

All data were analyzed using JAMOVI (Version 2.6.26). Continuous variables are expressed as mean ± SD or median (IQR), depending on normality (Shapiro–Wilk). Group comparisons were performed using Student’s t-test or Mann–Whitney U test, as appropriate; categorical variables were analyzed using χ² or Fisher’s exact tests. Group comparisons with more than two levels were analyzed using one-way ANOVA with post-hoc tests as appropriate. ROC curves and AUCs (95% CIs) were estimated for each protein. For the combined model, a multivariable logistic regression including all six proteins was fitted, and model-predicted probabilities were used to compute the combined ROC and AUC. Statistical significance was two-sided at p<0.05.

Participants with asthma exhibited significantly poorer sleep quality compared to healthy controls, as indicated by higher Children’s Sleep Habits Questionnaire (CSHQ) scores (>41), with 51.2% of asthmatic children demonstrating poor sleep quality versus only 14.7% in the control group (p=0.001) (Table 1). Despite these notable differences in sleep quality, other sociodemographic characteristics—such as age, gender distribution, body weight, height, body mass index (BMI), and exposure to secondhand smoke—did not differ significantly between the asthma and control groups (p>0.05) (Table 1).

The sociodemographic and laboratory characteristics of participants based on asthma control status are summarized in Table 2. No statistically significant differences were observed between children with well-controlled asthma and those with partially controlled or uncontrolled asthma regarding age, sex, body weight, height, or body mass index (BMI) (all p>0.05). Likewise, total IgE levels and asthma follow-up durations did not differ significantly between groups (p=0.10 and p=0.78, respectively). Eosinophil counts were also similar between groups (p=0.79). However, children with partially controlled or uncontrolled asthma exhibited significantly lower values for FEV₁ (liters), FEV₁/FVC, PEF (both % and liters), and MEF_25–75 (both % and liters) compared to well-controlled asthmatic children (p=0.004, p=0.006, p=0.04, p=0.01, p=0.03, and p=0.003, respectively) (Figure 1). Furthermore, poor sleep quality was significantly more common among children with partially controlled or uncontrolled asthma (p=0.001). Nighttime symptoms showed a near-significant difference between groups (p=0.07) (Table 2).

Asthmatic children displayed significantly elevated circadian rhythm proteins (BMAL1, CLOCK, CRY1, PER1, and PER2) compared to healthy controls (p<0.001, p<0.001, p=0.002, p=0.008, and p<0.001, respectively) (Table 3). However, CRY2 levels were similar between groups (p=0.72). These elevated protein levels suggest circadian dysregulation in children with asthma, potentially impacting airway inflammation and symptom severity.

Asthmatic children with poor sleep quality demonstrated significantly higher levels of the circadian rhythm proteins BMAL1, PER1, and PER2 compared to children with good sleep quality (p=0.005, p=0.01, and p<0.001, respectively) (Table 4). Although CLOCK protein levels exhibited a trend toward elevation in children with poor sleep quality, the difference was not statistically significant (p=0.30). CRY1 and CRY2 proteins were not significantly associated with sleep quality (p>0.05), indicating a selective impact of specific circadian proteins on sleep regulation.

When circadian rhythm proteins were analyzed based on sleep quality (CSHQ scores), significantly higher levels of BMAL1, PER1, and PER2 were observed among asthmatic children with poor sleep quality (CSHQ>41) compared to those with good sleep quality (CSHQ<41) (p=0.03, p=0.01, and p=0.02, respectively) (Table 5). However, no significant differences were found in CLOCK, CRY1, or CRY2 proteins according to sleep quality status (p>0.05). These results suggest a potential link between circadian rhythm disruptions and impaired sleep quality in pediatric asthma.

Significant associations were identified between asthma control and specific circadian rhythm proteins. Children with partially controlled or uncontrolled asthma had elevated levels of BMAL1, PER1, and PER2 proteins compared to well-controlled asthma patients (p=0.04, p=0.04, and p=0.006, respectively) (Table 6). In contrast, CLOCK, CRY1, and CRY2 proteins did not differ significantly according to asthma control status (p>0.05). These findings indicate the potential utility of specific circadian proteins as biomarkers for asthma severity and control.

ROC analysis revealed varying diagnostic performances of circadian rhythm proteins as biomarkers distinguishing asthmatic children from controls. Among single markers, PER2 and BMAL1 provided the strongest individual discrimination between asthma and controls (AUCs ≈0.75; p<0.01 vs. 0.5), whereas a multivariable model including all six proteins achieved a slightly higher AUC (~0.76). These differences should be interpreted as descriptive, as the study was not designed or powered to test head-to-head superiority among proteins. Lower diagnostic performances were noted for CRY1 (AUC=0.70; SE=0.06; 95% CI: 0.58–0.82) and PER1 (AUC=0.68; SE=0.06; 95% CI: 0.56–0.80). CRY2 exhibited the lowest diagnostic performance (AUC=0.63; SE=0.06; 95% CI: 0.51–0.75) (Figure 2).