Startseite Medizin Assessment of circadian rhythm protein levels in the pathogenesis of infantile colic
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Assessment of circadian rhythm protein levels in the pathogenesis of infantile colic

  • Feyza Kelleci Çelik ORCID logo EMAIL logo , Seyyide Doğan ORCID logo , Tahir Aydin ORCID logo , İbrahim Yilmaz ORCID logo und Derya Büyükkayhan ORCID logo
Veröffentlicht/Copyright: 15. September 2025

Abstract

Objectives

Infantile colic is a common disorder characterized by excessive crying in infants, with its etiology remaining largely unclear. In this study, the potential role of core circadian rhythm (CR) proteins in infantile colic was investigated.

Methods

Our study comprised 61 infants aged 0–3 months. Blood samples were collected intravenously from each individual. They were then centrifuged using the appropriate technique and divided into small portions. Cry1, Cry2, Per1, Per2, Clock, and Bmal1 protein levels in plasma samples were measured using enzyme-linked immunosorbent assay (ELISA) kits. A detailed questionnaire was administered to address various factors related to both the mother and the infant. The possible relationships between the measured CR protein levels and the gathered natal and sociodemographic data were then assessed using multiple regression analysis.

Results

The findings indicate that CR protein levels in infants with colic are significantly different from the control group, suggesting that CR dysregulation may be associated with colic symptoms. Furthermore, correlations were identified between these protein levels and variables, including vitamin D supplementation, nocturnal sleep patterns, the number of siblings, and maternal gestational diabetes. Statistically significant regression models, specifically those incorporating the Per2 variable, elucidated its associations with nocturnal sleep patterns, sibling counts, and gestational diabetes.

Conclusions

We argue that restoring CR protein levels to the normal range may improve the prognosis of infantile colic. These findings provide a novel perspective on the potential role of CR proteins in the infantile colic etiology, potentially paving the way for new treatment approaches in this area.

Introduction

The circadian rhythm (CR) is a repetitive cycle with periods of approximately 24 h that regulates various biological processes. In mammalian cells, these rhythms are generated and regulated by four main genes/proteins, including Cryptochrome (Cry1 and Cry2) and Period (Per1 and Per2), Circadian Locomotor Output Cycle Kaput (Clock), Brain-Muscle-Arnt-Like protein-1 (Bmal1) [1]. Numerous studies highlight a bidirectional relationship between the desynchronization of circadian clock genes/proteins and disease severity, where disruptions in CR can exacerbate various conditions. This may predispose individuals to many diseases, such as neurological, psychiatric, cardiometabolic, and immune disorders [2], obesity [3], insomnia, gastrointestinal system (GIS) disorders, and cancer [4], 5]. Since the link between CR disruption and diseases has been recognized, the treatment of these diseases has been reorganized [3].

The functions of the GIS are regulated by the circadian system and exhibit rhythmic changes. Colon motor activity increases in the morning; however, the secretion of gastric acid is heightened during the night. The gut microbiota in both humans and mice has been reported to display circadian oscillations [6]. Disruption of CR due to shift work or jet lag has been associated with GIS symptoms such as abdominal bloating, abdominal pain, diarrhea, and constipation [7]. Rao and Lin (2022) observed that Per1/2 double-knockout mice exhibited a loss of rhythmic muscle contractions in the intestine and rhythmic fluctuations in colon pressure [6]. CR genes/proteins could be potential cancer biomarkers [8]. Colon adenocarcinoma disease (COAD) is one of the cancer types closely associated with circadian disruption [6]. Bmal1 is reported as a tumor suppressor gene in COAD [9]. Mostafaie et al. (2009) revealed that individuals diagnosed with COAD exhibited elevated levels of the Clock gene [10]. Furthermore, several reports suggest that Cry2 and Per2 could be evaluated as potential prognostic biomarkers in COAD. Per2 has also been demonstrated to serve as a prognostic predictor in several human malignancies, including gastric, colorectal, and lung cancer [11], [12], [13]. These findings indicate that CR genes/proteins responsible for biological rhythms are directly linked to GIS physiology [14].

Disruptions in the circadian system may contribute to the development of infantile colic, a condition of unknown etiology that is likely multifactorial [15]. Besides, hormonal, neurodevelopmental, psychological, and gastrointestinal variables are among the hypothesized causes [16]. Infantile colic is a clinical syndrome observed in infants aged 0–3 months, characterized by episodes of restlessness, agitation, and excessive crying, particularly during the late afternoon and evening hours. These episodes occur for at least 3 h a day, three days a week, and persist for a minimum of 3 weeks, without any identifiable underlying cause [14].

Studies on hormones, particularly components of the sleep-wake cycle such as serotonin and melatonin, associated with intestinal motility in the pathophysiology of infantile colic, are remarkable [17], 18]. However, while the levels of these hormones have been examined in infants with colic, the role of CR genes/proteins involved in their regulation remains poorly understood. This highlights a critical gap in understanding the mechanisms underlying infantile colic. Our study aims to investigate the levels of CR core proteins in infantile colic and gain a novel perspective on the pathophysiology of the disease. In addition, we analyzed the effects of vitamin D supplementation in infants and the use of propofol and curareen during caesarean deliveries on biological rhythm disruptions. Our sample consisted of infants diagnosed with infantile colic and healthy controls. Plasma samples of the participants were collected and the levels of CR proteins Cry1, Cry2, Per1, Per2, Clock, and Bmal1 were measured. We aim for the findings to enhance understanding of the mechanisms behind infantile colic and guide the development of novel therapeutic approaches by targeting the regulation of the CR gene and protein.

Materials and methods

Materials

We employed enzyme-linked immunosorbent assay (ELISA) kits for Human Cry1, Cry2, Per1, Per2, Clock, and Bmal1 from BT LAB (Shanghai Korain Biotech Co., Ltd., China).

Methods

The study protocol

Our study comprised 61 infants aged 0–3 months admitted to the Department of Paediatrics, Health Sciences University Haseki Training and Research Hospital between January 2023 and May 2023. The patient group consisted of 31 infants diagnosed with infantile colic based on the Wessel criteria. The Wessel criteria for infantile colic is a clinical evaluation method used to diagnose colic in infants and to distinguish colic crying from normal crying. According to these criteria, infantile colic is defined as crying episodes lasting more than 3 h a day, continuing for at least 3 days a week, and persisting for at least 3 weeks [14]. Samples for the control group were randomly collected from 30 healthy infants attending the hospital for routine screening. Infants older than 3 months and those with genetic, metabolic, or chronic conditions, any other medical problem, prematurity, birth complications, a history of intensive care, and lack of parental consent were excluded. We excluded infants with other diseases potentially affecting colic symptoms to ensure the study focused solely on the relationship between colic and CR.

We adhered to the Helsinki Declaration, developed by the World Medical Association. The Faculty of Medicine Local Scientific Medical Research Ethics Committee of Karamanoğlu Mehmetbey University approved our study protocol with the decision numbered 01–2021/02. This study was performed with the support of the project number 2021/132 of the University of Health Sciences Scientific Research Projects Unit.

Participant information

Participants’ families completed a questionnaire designed to gather detailed information before blood samples were collected. The questionnaire covered participant characteristics, including gestational duration, mode of delivery, birth weight, age, gender, weight, type of nutrition (breast milk, infant formula, or a combination of both (bm/fm/bm+fm)), vitamin D supplementation, nocturnal sleep patterns, and sibling count. Additionally, maternal information such as age, smoking or alcohol history, chronic diseases, gestational diabetes, and postpartum depression was recorded. Household details were also assessed, including the number of occupants and the type of heating system used.

Plasma sample preparation

All samples were collected in the late afternoon hours (4:00-6:00 pm), as complaints increased in the afternoon-evening hours. Following the determination of the study groups, blood samples were collected intravenously from each individual into sterile tubes containing either heparin or lithium (BD Vacutainer®, green cap). Then, they were centrifuged for 10 min at 2000 g at 4 °C and divided into small portions. Collected plasma specimens were stored at −80 °C until analysis, with a maximum storage time of 3 months [19].

Measurement of circadian rhythm protein levels

CR protein levels were measured using commercially available ELISA kits for Cry1 (detection ranges: 15–3000 ng/L, sensitivity: 7.31 ng/L), Cry2 (detection ranges: 10–2000 ng/L, sensitivity: 5.25 ng/L), Per1 (detection ranges: 7–1500 ng/L, sensitivity: 4.03 ng/L), Per2 (detection ranges: 15–3000 ng/L, sensitivity: 7.37 ng/L), Clock (detection ranges: 10–2000 ng/L, sensitivity: 4.08 ng/L), and Bmal1 (detection ranges: 20–6000 ng/L, sensitivity: 13.32 ng/L). The intra-assay and inter-assay coefficients of variation (CVs, %) for the ELISA kits were below 8 and 10 %, respectively. No pre-dilution procedure was performed on the samples. An ELISA kit was applied in triplicate to quantitatively determine the concentration of Cry1, Cry2, Per1, Per2, Clock, and Bmal1 in plasma.

Disruptions in the CR gene expression lead to alterations in the levels of proteins encoded by these genes. The CR protein levels measured in biological samples, including plasma and organs, provide valuable insights into alterations in CR gene expression. Moreover, CR proteins are crucial for maintaining the normal functioning of the biological clock in peripheral tissues, and fluctuations in their levels are linked to various pathological conditions [19]. Therefore, CR protein levels in plasma were evaluated in this study.

Statistical analysis

Ready-made statistical tables were used to determine the sample size [20]. Statistical analysis was performed using SPSS Statistics 21. Descriptive statistics were presented for all data analyzed. Normality was determined using the Shapiro-Wilk test. The Mann-Whitney U and t-test tests were used to test for differences between groups. Results were presented as medians and interquartile ranges (Q1-Q3). Spearman’s rho correlation and Eta Coefficient measure the strength and direction of association between two variables. Multiple linear regression (MLR) analysis was applied to determine the variables affecting the CR protein level. p-Values below 0.05 and 0.10 were considered as significant.

Results

A total of 61 participants, including the control group (n=30) and the patient group (n=31), completed the study. Information was collected from the participants’ families under the main headings of participant characteristics, maternal information, and household details before blood samples were taken (Table 1).

Table 1:

Some natal and sociodemographic characteristics of participants.

Variables Control group Patient group p-Value
Mean ± SD or n (%) Mean ± SD or n (%)
Participant characteristics

Duration of gestation, week 37.97 ± 1.16 37.68 ± 2.29 0.933
Mode of delivery
 Normal spontaneous vaginal 12 (40) 14 (45.17) 0.798
 Cesarean section 18 (60) 17 (54.83)
Birth weight, g 3053 ± 200.80 3205 ± 435.0 0.559
Age, days 42.23 ± 11.81 39.58 ± 12.21 0.419
Gender
 Female 9 (30) 12 (38.70) >0.999
 Male 21 (70) 19 (61.30)
Weight, g 4477 ± 534.20 4656 ± 639.8 0.276
Nutrition
 Breast milk 22 (73.33) 12 (38.71) 0.007 a
 Infant formula 3 (10) 5 (16.13)
 Breast milk + infant formula 5 (16.67) 14 (45.16)
Vitamin D support
 Yes 11 (36.7) 21 (67.7) 0.021 a
 No 19 (63.3) 10 (32.3)
Nocturnal sleep, hours 7.30 ± 0.65 6.64 ± 0.91 0.036 a
Sibling count 2.833 ± 1.289 2.548 ± 0.9605 0.433

Maternal information

Age, year 27.07 ± 4.608 27.03 ± 4.943 0.754
Smoking/alcohol history
 Yes 8 (24) 4 (13) 0.211
 No 22 (76) 27 (87)
Chronic disease
 Yes >0.999
 No 30 (100) 31 (100)
Gestational diabetes 7 (23.3) 4 (13) 0.336
Postpartum depression 6 (20) 10 (32) 0.384

Household details

Population 4.967 ± 1.377 4.968 ± 1.169 0.879
Heating system
 Natural gas 30 (100) 30 (100) >0.999
 Stove heating
 Others
  1. ap<0.05, bp<0.10. SD, standard deviation; n, number of participants. Results are shown as mean ± SD or n (%). Bold values indicate statistically significant results obtained from the Mann–Whitney U test.

The mean ages of the infants in the control and patient groups were 42.23±11.81 days and 39.58±12.21 days, respectively. Of the total participants, 34.40 % were female (n=21) and 65.60 % were male (n=40). 30 percent of the control group was female (n=9) and 70 percent was male (n=21). The patient group consisted of 38.70 % females (n=12) and 61.30 % males (n=19). No significant differences were observed between the groups for variables other than nutrition, vitamin D supplementation, and nocturnal sleep, as shown in Table 1. While 73.33 % of the subjects in the control group were breastfed, this rate was 38.71 % in the patient group (p=0.01). In addition, most 0subjects with colic (45.16 %) were fed infant formula in addition to breast milk (p=0.01). While 67.7 % of the subjects in the colic group received vitamin D supplementation, this rate was 36.7 % in the control group (p=0.02). Night sleep duration was significantly longer in the control group than in the patient group (p=0.04).

Circadian rhythm protein levels

The changes in CR protein levels in plasma samples between the groups are shown in Table 2 and Figure 1. Analysis of the test statistics revealed statistically significant differences between the groups for Cry1 (U=265.5, p=0.058), Cry2 (t-test=2.188, p=0.033), Per2 (U=261, p=0.049), Clock (U=260.5, p=0.048), and Bmal1 (U=529, p=0.050) protein levels. Conversely, no statistically significant difference was observed for Per1 (U=458, p=0.920) between the comparison groups. Cry1 and Per2 levels were significantly increased in the patient group compared to the control group (p=0.058 and p=0.049, respectively). Cry2 level was decreased in the patient group compared to the control group (p=0.033). The patient group exhibited a significant increase in Clock protein levels, accompanied by a notable decrease in Bmal1 protein levels (p=0.048 and p=0.050, respectively).

Table 2:

CR protein levels in plasma samples.

Protein Control group, ng/L Median (Q1–Q3) Patient group, ng/L Median (Q1-Q3) p-Value
Cry1 200 (150–313.34) 253.33 (193.33–638.89) 0.058 b
Cry2 71.25 (45.94–88.13) 54.38 (40.31–69.37) 0.033 a
Per1 140.45 (107.32–202.53) 135.38 (111.72–157.58) 0.920
Per2 329.94 (260.68–471.15) 407.54 (314.66–598.85) 0.049 a
Clock 390 (124.58–520) 495 (233.33–624.19) 0.048 a
Bmal1 791.84 (518.56–1061.79) 635.12 (468.77–1649.85) 0.050 a
  1. Results are shown as median (Q1-Q3) (Q1: 25th percentile; Q3: 75th percentile). CR protein levels are expressed in ng/L., CR, circadian rhythm. Normality tests (Kolmogorov-Smirnov p=0.200, Shapiro-Wilk p=0.315) indicated that the Cry2 variable was normally distributed. Therefore, the p-value reported for Cry2 was obtained from an independent samples t-test. For all other variables, which did not exhibit normal distribution, the Mann-Whitney U test was applied. Bold values indicate statistically significant results (ap<0.05; bp<0.10).

Figure 1: 
Comparison of circadian rhythm proteins in control and patient groups. (A) Cry1 level in control and patient groups; (B) Cry2 level in control and patient groups; (C) Per1 level in control and patient groups; (D) Per2 level in control and patient groups; (E) clock level in control and patient groups; (F) Bmal1 level in control and patient groups. Results are shown as median (Q1-Q3) (Q1: 25th percentile; Q3: 75th percentile). Circadian rhythm protein levels are expressed in ng/L. Mann–whitney U test, *p<0.05.
Figure 1:

Comparison of circadian rhythm proteins in control and patient groups. (A) Cry1 level in control and patient groups; (B) Cry2 level in control and patient groups; (C) Per1 level in control and patient groups; (D) Per2 level in control and patient groups; (E) clock level in control and patient groups; (F) Bmal1 level in control and patient groups. Results are shown as median (Q1-Q3) (Q1: 25th percentile; Q3: 75th percentile). Circadian rhythm protein levels are expressed in ng/L. Mann–whitney U test, *p<0.05.

Correlation analysis results

In addition to highlighting differences in CR protein levels between the groups, we investigated their correlations with selected natal and sociodemographic variables. Regarding Cry1 levels, a statistically significant positive correlation was observed with vitamin D supplementation (η=0.376, p=0.037). This indicates that approximately 37.6 % of the variance in Cry1 levels can be explained by differences in vitamin D supplementation status. Furthermore, Cry1 levels exhibited a weak to moderate positive correlation with nocturnal sleep (Spearman’s rs=0.345, p=0.058) and sibling count (Spearman’s rs=0.343, p=0.059), suggesting a tendency towards higher Cry1 levels with increased nocturnal sleep duration and sibling count. No other statistically significant correlations were found between Cry1 levels and other variables (Table 3).

Table 3:

Relationship between plasma Cry1 and Cry2 levels and selected variables.

Cry1 level Cry2 level
Spearman’s rs p-Value Spearman’s rs p-Value
Age, days 0.107 0.565 Age (days) −0.130 0.484
Duration of gestation 0.049 0.794 Duration of gestation 0.163 0.380
Nocturnal sleep 0.345 0.058 b nocturnal sleep 0.083 0.656
Sibling count 0.343 0.059 b Sibling count −0.024 0.898
Eta (η) Eta(η)
Gestational diabetes 0.125 0.503 Gestational diabetes 0.077 0.680
Postpartum depression 0.043 0.819 Postpartum depression 0.291 0.102
Vitamin D supp. 0.376 0.037 a Vitamin D supp. 0.059 0.752
bs/fm/bs+fm 0.341 0.234 bs/fm/bs+fm 0.102 0.864
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are represented as Spearman’s rho correlation coefficients (rs) and p-values. Eta coefficient: sum of squares between groups/total and p-values show ANOVA significances. Bold values indicate the statistical significance of Spearman’s correlation and Eta coefficients (ap<0.05; bp<0.10).

Conversely, Cry2 levels did not demonstrate any statistically significant correlations with any of the tested variables in the model.

A moderate positive correlation was observed between Per1 and Per2 levels and the gestational diabetes (η=0.365, p=0.043 and η=0.401, p=0.025). Per2 levels exhibited moderate negative correlation with age (Spearman’s rs=−0.326, p=0.074). No correlation was noted between Per1 and Per2 and other variables (Table 4).

Table 4:

Relationship between plasma Per1 and Per2 levels and selected variables.

Per1 level Per2 level
Spearman’s rs p-Value Spearman’s rs p-Value
Age, days −0.048 0.797 Age (days) −0.326 0.074 b
Duration of gestation 0.086 0.644 Duration of gestation 0.248 0.178
nocturnal sleep 0.102 0.585 nocturnal sleep 0.190 0.307
Sibling count 0.234 0.206 Sibling count −0.003 0.986
Eta(η) Eta(η)
Gestational diabetes 0.365 0.043 a Gestational diabetes 0.401 0.025 a
Postpartum depression 0.156 0.403 Postpartum depression 0.139 0.170
Vitamin D supp. 0.180 0.334 Vitamin D supp. 0.075 0.688
bs/fm/bs+fm 0.353 0.155 bs/fm/bs+fm 0.343 0.170
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are represented as Spearman’s rho correlation coefficients (rs) and p-Values. Eta coefficient: sum of squares between groups/total and p-values show ANOVA significances. Bold values indicate the statistical significance of Spearman’s correlation and Eta coefficients (ap<0.05; bp<0.10).

There was a moderate positive correlation between Clock level and sibling count (Spearman’s rs=0.379, p=0.035) and vitamin D supplementation (η=0.496, p=0.005). Conversely, Clock levels exhibited moderate negative correlation with age (Spearman’s rs=−0.388, p=0.031), suggesting that Clock levels tend to decrease as infant age increases. No other significant correlations were found for Bmal1 levels with variables (Table 5).

Table 5:

Relationship between plasma Clock and Bmal1 levels and selected variables.

Clock level Bmal1 level
Spearman’s rs p-Value Spearman’s rs p-Value
Age, days −0.388 0.031 a Age (days) −0.022 0.908
Duration of gestation 0.105 0.573 Duration of gestation −0.048 0.796
Nocturnal sleep 0.079 0.647 Nocturnal sleep 0.086 0.647
Sibling count 0.379 0.035 a Sibling count 0.197 0.287
Eta (η) Eta (η)
Gestational diabetes 0.010 0.958 Gestational diabetes 0.083 0.656
Postpartum depression 0.067 0.719 Postpartum depression 0.172 0.356
Vitamin D supp. 0.496 0.005 a Vitamin D supp. 0.299 0.102
bs/fm/bs+fm 0.392 0.109 bs/fm/bs+fm 0.089 0.894
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are represented as Spearman’s rho correlation coefficients (rs) and p-Values. Eta coefficient: sum of squares between groups/total and p-values show ANOVA, significances. Bold values indicate the statistical significance of Spearman’s correlation and Eta coefficients (ap<0.05; bp<0.10).

Multiple linear regression results

MLR analysis was performed to comprehensively examine the effect of natal and sociodemographic variables on CR protein levels. Tables 6 and 7 show the results of a regression analysis examining the relationship between plasma, the identified variables, and plasma Cry1 and Cry2 levels, respectively. This analysis revealed that none of the evaluated factors had a statistically significant impact on the levels of Cry1 and Cry2 proteins.

Table 6:

Regression analysis results for plasma Cry1 levels and selected variables.

Variables β SE p-Value
Constant −459.568 3457.629 0.895
Age, days −0.302 13.524 0.982
bs/fm/bs+fm 32.004 133.471 0.813
Duration of gestation 2.867 70.709 0.968
Nocturnal sleep 232.877 135.866 0.101
Vitamin D supp. −434.896 31.4563 0.181
Sibling count −54.633 155.060 0.728
Gestational diabetes 184.679 387.550 0.638
Postpartum depression 71.837 308.275 0.818
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE) and p-value. Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Model statistics: R2=0.257, F=0.952, p-value=0.496.

Table 7:

Regression analysis results for plasma Cry2 levels and selected variables.

Variables Β SE p-Value
Constant 5.396 115.482 0.963
Age, days −0.226 0.451 0.621
bs/fm/bs+fm −1.467 4.457 0.745
Duration of gestation 1.617 2.361 0.500
Nocturnal sleep 1.996 4.537 0.664
Vitamin D supp. −4.945 10.506 0.642
Sibling count −4.944 5.178 0.467
Gestational diabetes −5.179 12.943 0.692
Postpartum depression 15.306 10.296 0.151
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE) and p-value. Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Model statistics: R2=0.186, F=0.627, p-value=0.746.

Table 8 explains the relationships between plasma Per1 level and the natal and sociodemographic variables. The MLR model for plasma Per1 levels was statistically significant (F=2.711, p=0.030), explaining 49.6 % of its variance (R2=0.496). Significant predictors included nocturnal sleep, sibling count, and gestational diabetes (p<0.05). Nocturnal sleep duration negatively predicted Per1 levels, while the number of siblings and the presence of gestational diabetes were significant positive predictors. However, other variables were not statistically significant (Table 8).

Table 8:

Regression analysis results for plasma Per1 levels and selected variables.

Variables β SE p-Value
Constant 1014.189 805.602 0.221
Age, days −3.363 3.150 0.297
bs/fm/bs+fm −51.234 31.097 0.113
Duration of gestation −7.544 16.474 0.651
Nocturnal sleep −74.775 31.655 0.027 a
Vitamin D supp. −25.200 73.290 0.734
Sibling count 76.579 36.127 0.045 a
Gestational diabetes 214.404 90.296 0.026 a
Postpartum depression 8.477 71.825 0.907
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE) and p-value. Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Model statistics: R2=0.496, F=2.711, p-value=0.030.

Table 9 shows the relationships between plasma Per2 levels and selected variables. Similarly, the regression analysis for plasma Per2 levels also revealed a statistically significant model, with notable findings among its predictors. The model for plasma Per2 levels was statistically significant (F=2.289, p=0.059), explaining 45.4 % of its variance (R2=0.454). Significant predictors included nocturnal sleep, sibling count, and gestational diabetes (p<0.05). Nocturnal sleep was negatively associated with Per2 levels, while sibling count and gestational diabetes showed positive associations. However, other variables were not statistically significant (Table 9).

Table 9:

Regression analysis results for plasma Per2 levels and selected variables.

Variables β SE p-Value
Constant 2638.854 2246.983 0.252
Age, days −11.840 8.788 0.191
bs/fm/bs+fm −119.577 86.738 0.181
Duration of gestation −23.996 45.950 0.606
Nocturnal sleep −164.102 88.293 0.076 b
Vitamin D supp. 41.111 204.422 0.842
Sibling count 188.079 100.767 0.075 b
Gestational diabetes 649.660 251.854 0.017 a
Postpartum depression 82.042 200.336 0.686
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE) and p-value. Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Model statistics: R2=0.454, F=2.289, p-value=0.059.

The results of the MLR model for Clock levels and selected variables were analyzed, as shown in Table 10. The Clock model was not statistically significant (F=1.854, p=0.120), explaining only 40.3 % of its variance (R2=0.403). Age and vitamin D supplementation were identified as negative predictors of Clock levels (p<0.05).

Table 10:

Regression analysis results for plasma Clock levels and selected variables.

Variables β SE p-Value
Constant 2471.651 1610.707 0.139
Age, days −13.232 6.299 0.047 a
bs/fm/bs+fm −12.372 62.176 0.844
Duration of gestation −33.613 32.939 0.318
Nocturnal sleep 17.343 63.291 0.786
Vitamin D supp. −319.987 146.536 0.039 a
Sibling count 41.313 72.233 0.573
Gestational diabetes 44.775 180.536 0.806
Postpartum depression 161.153 143.607 0.273
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE) and p-value. Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Model statistics: R2=0.403, F=1.854, p-value=0.120.

Table 11 presents the relationship between plasma Bmal1 levels and selected variables. For plasma Bmal1 levels, the MLR model was not statistically significant (F=0.667, p=0.715), explaining only 19.5 % of its variance (R2=0.195). In this model, no individual predictor achieved statistical significance for association with Bmal1 levels. Vitamin D supplementation was a positive predictor, which was statistically significant at the p<0.10 level (p=0.061).

Table 11:

Regression analysis results for plasma Bmal1 levels and selected variables.

Variables β SE p-Value
Constant −168.616 1560.360 0.914
Age, days −1.060 6.103 0.863
bs/fm/bs+fm −13.260 60.233 0.827
Duration of gestation 3.195 31.909 0.921
nocturnal sleep 24.478 61.313 0.693
Vitamin D supp. 279.387 141.955 0.061 b
Sibling count 80.689 69.975 0.261
Gestational diabetes −0.072 174.893 0.996
Postpartum depression 92.652 139.118 0.512
  1. bm/fm/bm+fm: breast milk/infant formula/breast milk+infant formula. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE) and p-value. Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Model statistics: R2=0.195, F=0.667, p-value=0.715.

Initial efforts to construct MLR models for various CR protein levels indicated that most models failed to meet the necessary statistical assumptions for robust interpretation. Consequently, stepwise regression procedures were employed to improve model performance. Notably, among the proteins analyzed, a statistically sound and practically interpretable model was successfully achieved only for plasma Per1, Per2, and Clock levels.

Table 12 presents the findings from the stepwise MLR analysis, which aimed to identify variables significantly associated with plasma Per1, Per2, and Clock levels. The overall regression model was found to be statistically significant, indicating that the included predictors collectively explain a significant portion of the variance in plasma Per1, Per2, and Clock levels. The models achieved R2 values of 0.390, 0.339, and 0.249, respectively. Despite the observed associations, among all models tested, only the Per2 model fulfilled essential statistical assumptions (multicollinearity, independence of residuals, homoscedasticity, normality of residuals).

Table 12:

Stepwise regression analysis results for plasma Per1, Per2, and Clock levels and selected variables.

Per 1 Variables β SE p-Value
Constant 414.390 202.323 0.050a
Nocturnal sleep 233.022 81.012 0.008a
Sibling count 86.028 29.496 0.007a
Gestational diabetes −71.832 30.077 0.027a
Per 2 Variables β SE p-V alue
Constant 1103.531 564.318 0.060 b
Nocturnal sleep −165.063 85.842 0.065 b
Sibling count 188.801 82.270 0.029 a
Gestational diabetes 657.944 225.957 0.007 a
Clock Variables β SE p-V alue
Constant 971.994 158.872 0.000 a
Vitamin D supp. −348.555 113.256 0.005 a
  1. Data are presented as multiple linear regression (MLR) coefficients (β) with standard error (SE). Bold values indicate statistically significant regression coefficients (ap<0.05; bp<0.10). Per1 model statistics: R2=0.390, F=5.760, p-value=0.004; Per2 model statistics: R2=0.339, F=4.619, p-value=0.010; Clock model statistics: R2=0.246, F=9.471, p-value=0.005.

To assess multicollinearity among the independent variables, Variance Inflation Factors (VIF) were calculated. The VIF values for nocturnal sleep (1.075), gestational diabetes (1.021), and sibling count (1.061) were all substantially below the commonly accepted threshold of 4 (or 5), indicating that multicollinearity was not a significant concern in the model.

The independence of residuals was examined using the Durbin-Watson statistic. A value of 1.768, which falls within the acceptable range between the lower bound (du=1.65) and the upper bound (4−du=2.35), suggested no evidence of autocorrelation among the residuals.

Homoscedasticity, the assumption that the variance of the residuals is constant across all levels of the independent variables, was assessed using White’s test. The calculated White’s test statistic was 17.734. Given a critical chi-square table value of 19.02 (degrees of freedom: 9), the test statistic was lower than the critical value. This indicates that the null hypothesis of homoscedasticity could not be rejected, and thus, the assumption of constant error variance was met, with no evidence of heteroscedasticity in the data. Finally, the assumption of normality of residuals was also assessed and deemed satisfactory (Shapiro-Wilk test: 9.956 and p-value: 0.273).

Among the variables retained in the Per2 model, gestational diabetes emerged as a highly significant positive predictor of plasma Per2 levels (β=657.944, SE=225.957, p=0.007). This indicates that the presence of a maternal gestational diabetes history significantly increases Per2 levels by 657.944 units. Sibling count was also identified as a statistically significant positive predictor (β=188.801, SE=82.270, p=0.029). This indicates that for each additional sibling, Per2 levels are estimated to increase by 188.801 units. Nocturnal sleep, while statistically significant at the conventional α=0.10, emerged as a highly significant negative predictor of plasma Per2 levels (β=−165.063, SE=85.842, p=0.065). This indicates that for each additional hour of nocturnal sleep, Per2 levels are estimated to decrease by 165.063 units. The constant (1,103.531) represents the predicted mean Per2 level when all included predictors are at a value of zero, though its significance (p=0.060) is borderline.

Discussion

Although infantile colic is classified as a functional disorder with an unidentified etiology and multifactorial mechanisms, regulating CR may have benefits in managing these complex processes [15]. Recent studies focusing on hormones such as melatonin and serotonin suggest that infantile colic is associated with circadian misalignment; biological clock desynchronization may be linked to colic symptoms. Serotonin causes smooth muscle contraction, while melatonin induces relaxation. According to Engler et al. (2014), although the effect of serotonin in infants begins during the first weeks after birth, the melatonin cycle is regulated after the third month. As a result, melatonin cannot counteract serotonin’s contraction-inducing effects during the first three months, leading to severe intestinal contractions, particularly in the late afternoon and evening [17]. Similarly, İnce et al. (2018) observed reduced melatonin levels in colic infants, whereas Brett et al. (2024) reported elevated cortisol concentrations [15], 18]. Furthermore, Cuesta et al. (2008) demonstrated that the administration of serotonergic receptor agonists, which induce intestinal motility and smooth muscle contraction, led to a decrease in Per1 and Per2 expression [21]. In our study, Cry2 and Bmal1 levels were significantly decreased (p=0.033 and p=0.050) in the colic group, whereas Cry1, Per2, and Clock levels were significantly elevated (respectively, p=0.058, p=0.049, and p=0.049). In healthy individuals, CR genes/proteins exhibit synchronized oscillations, although variations may occur due to environmental and metabolic factors. Several studies have reported fluctuations in these genes/proteins, particularly among patients with COAD [10], 13], 22]. He et al. (2022) reported that the expression of Clock and Cry1 was elevated, whereas Bmal1, Cry2, and Per1 were diminished in patients diagnosed with COAD. COAD patients with higher Cry2 and Per2 expression levels exhibited a worse prognosis. This suggests that the Cry2 and Per2 genes/proteins may contribute to tumor formation or accelerate cancer progression in the colon. Therefore, the elevation of Cry2 and Per2 gene/protein levels can be considered potential prognostic biomarkers [13]. Recent findings suggest that suppressing the Bmal1 gene in COAD promotes cell invasion, indicating that Bmal1 acts as a tumor suppressor. Additionally, high Bmal1 expression levels have been associated with longer lifespans in COAD patients compared to lower levels [9]. Another study supporting our findings revealed that individuals with COAD exhibited elevated Clock gene expression and decreased Per1 levels [10]. Yoon et al. (2025) suggested that the increase in Clock and Cry1 expression contributes to cancer formation, while the increase in Bmal1, Per1, Per2, and Cry2 expression demonstrates a tumor-suppressing effect. Additionally, the same study reported that the loss of Bmal1 was associated with the downregulation of the Per1, Per2, and Per3 genes [8]. Based on this information, we hypothesized that the fluctuations in Per2 observed in our patient group may result from the suppression of Bmal1. In certain instances, the expression of specific genes may demonstrate opposite alterations. If one CR gene is downregulated, another functionally synchronized gene may show a compensatory increase. Failure of the compensatory mechanism can elevate the risk of developing pathological processes. Based on the common findings of our study and other studies, we suggest that the decrease in Cry2 and Bmal1 protein levels, and the increase in Cry1, Per2, and Clock protein levels, may have a potential relationship with GIS diseases.

The majority of colicky infants were fed with infant formula in addition to breast milk, whereas most infants in the healthy group were breastfed (p<0.05). Breastfeeding has been shown to improve nocturnal sleep due to its melatonin content, thereby reducing colic symptoms [23]. Infants aged 0–3 months receive melatonin through breast milk due to their physiological deficiency. An absence of melatonin from breast milk has been associated with various pathologies, particularly colic [24].

In this study, moderate positive correlations were observed between Cry1 levels and nocturnal sleep, sibling count, and vitamin D supplementation (p<0.05). Per1 and Per2 levels showed moderate positive correlations with gestational diabetes, while Per2 levels exhibited a negative correlation with age. Additionally, Clock levels demonstrated moderate correlations with sibling count and vitamin D supplementation (positive), as well as with age (negative). The Per1, Per2, and Clock genes have significant effects on sleep regulation and metabolic disease (obesity risk) [23], [24], [25], [26]. While their associations with sleep disorders and certain metabolic risk factors are well established, their direct relationships with sibling count, vitamin D supplementation, and gestational diabetes have not been clearly demonstrated in current research. To the best of our knowledge, our study provides a novel clinical contribution by investigating these variables and demonstrating moderate associations, thereby offering preliminary evidence for their potential relevance in regulating circadian protein expression. As a routine practice, vitamin D supplementation is provided to infants when the nutrient content of breast milk is insufficient [27]. In this study, we found that a higher proportion of infants in the colic group received vitamin D supplementation compared to the control group (p=0.021). Our findings indicated a positive relationship between vitamin D supplementation and increased levels of Cry1 and Clock proteins, emphasizing caution with high doses in pharmaceutical toxicology. Further research using larger data sets is required to elucidate the relationship between vitamin D, colic, and CR.

Regression analyses were conducted to assess the associations between plasma CR protein levels, including Cry1, Cry2, Per1, Per2, Clock, and Bmal1, and various natal and sociodemographic variables. None of the variables in the models had a significant effect on plasma Cry1 and Cry2 levels (p>0.05), suggesting that the analyzed variables may not play a substantial role in determining these protein levels. An increase in night-time sleep duration was found to significantly decrease Per1 and Per2 levels, whereas Per1 and Per2 levels were significantly higher in women with gestational diabetes and higher sibling counts. Clock levels were found to decrease significantly with increasing age and were significantly lower in individuals receiving vitamin D supplementation. However, although the Per1 and Clock models met statistical criteria such as model fit and coefficient significance, they failed to satisfy key regression assumptions and are therefore not considered reliable. A larger sample size, the inclusion of different variables, or alternative analytical methods may provide a more comprehensive understanding of these relationships. Only the Per2 model was both statistically significant and met all necessary assumptions. This model demonstrated that gestational diabetes, sibling count, and nocturnal sleep exert strong effects on Per2 levels. Similarly, Hoang et al. (2021) reported that alterations in Per2 contribute to variable sleep phenotypes [28]. We claim that managing gestational diabetes and increasing night-time sleep duration may reduce colic symptoms. Our study found that having more siblings may be associated with the development of infantile colic. In larger family structures, environmental factors such as more frequent disruptions in the infant’s sleep–wake cycle or irregular caregiving routines may contribute to this association. Additionally, factors such as genetic predispositions, parental stress levels, and variations in parenting styles should also be considered.

Our study has some limitations, including that the sample consisted of infants and only one blood sample could be collected per participant during the day. Although this situation limits the examination of changes in CR during the day, this limitation can be ignored since the samples were collected from both groups at similar times. The study was conducted with infants aged 0–3 months, and obstacles such as parental leave limited the sample size. Our sample size is consistent with the research on the subject in the literature [18]. On the other hand, expanding the sample size could lead to more comprehensive results.

Despite its high prevalence, the pathogenesis of infantile colic remains incompletely understood [18]. Due to the uncertainties surrounding its etiology, no single effective treatment method has been developed for colic. The development of synthetic CR proteins as part of drug formulations may offer a novel pharmacotherapeutic approach to managing this condition. Innovative methodologies for modulating the mammalian circadian clock with synthetic molecules are being developed. Synthetic derivatives of CR proteins have been produced, such as KL001 and KL044 for Cry1 and Cry2, KL101 and KL201 for Cry1, TH301 for Cry2, and KS15 for Clock-Bmal1. CR modulators are expected to have significant market potential in the pharmaceutical industry, and reports suggest that these molecules could offer drug candidates for various conditions, including jet lag, shift work, metabolic diseases, cancer, Alzheimer’s disease, and Parkinson’s disease [29]. The findings of our study may contribute to developing a treatment strategy for infantile colic by activating Bmal1 and Cry2 or suppressing Clock and Per2, using their synthetic derivatives.

This study is the first to explain that infantile colic may be linked to biorhythm disruption through the primary rhythm-regulating proteins, thereby addressing a critical gap in the field and offering an innovative perspective on treatment. This study, which contributes to the understanding of the pathophysiology of the disease, aims to prevent unnecessary examinations and applications, while improving sleep patterns and quality of life for both infants with colic and their parents.

Conclusions

In this study, differences were found in the levels of CR proteins Cry1, Cry2, Per2, Clock, and Bmal1 in infants with colic. Our results indicate that CR proteins are influenced by clinical and sociodemographic factors, with gestational diabetes, nocturnal sleep duration, number of siblings, and vitamin D supplementation emerging as the most significant variables. Our study will form a basis for further studies elucidating the disease mechanism and developing new treatment strategies for infantile colic. This research may also pave the way for new studies exploring how changes in the levels of CR-related genes/proteins could play a role not only in infantile colic but also in the etiology of various other diseases. Our findings suggest that regulation of circadian oscillators could be an effective approach for the prevention and management of diseases with limited treatment options, such as colic.


Corresponding author: Feyza Kelleci Çelik, Department of Anesthesia, Vocational School of Health Services, Karamanoglu Mehmetbey University, Karaman, Türkiye, E-mail:

Funding source: This study was carried out with the support of the project number 2021/132 of the University of Health Sciences Scientific Research Projects Unit

Award Identifier / Grant number: 2021/132

  1. Research ethics: Our study was carried out following The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans, and the study protocol was approved by the Faculty of Medicine Local Scientific Medical Research Ethics Committee of Karamanoğlu Mehmetbey University with the decision numbered 01-2021/02.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: Not applicable.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This study was carried out with the support of the project number 2021/132 of the University of Health Sciences Scientific Research Projects Unit.

  7. Data availability: Not applicable.

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Received: 2025-01-12
Accepted: 2025-08-09
Published Online: 2025-09-15
Published in Print: 2025-10-27

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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