1. INTRODUCTION
In most parts of the world, celiac disease (CeD) is recognized as one of the most prevalent chronic intestinal disorders. This condition is also known as gluten-sensitive enteropathy [1]. CeD is a growing burden worldwide, as indicated by its increasing prevalence and incidence, which has reached more than 2.9% globally [2]. Clinically, the morbidity and mortality risk caused by CeD can reach 11 per 1,000 people [3,4]. CeD also has a significant economic impact at both the social and health care levels [5]. There is currently no cure for CeD. For these reasons, the consideration of dietary manipulation has received increasing attention.
In susceptible individuals exposed to wheat gluten through the diet, an autoimmune reaction underpinning the onset of CeD is triggered by a combination of environmental factors and genetic vulnerability [6]. Gluten is only partially broken down by the gut lumen; it crosses the gut epithelial barrier to enter the lamina propria [7]. The impacts and properties of gluten peptides are cytotoxic, immunomodulatory, and permeabilizing [8].
The etiology of CeD involves antigen-presenting cells that have particular human leukocyte antigen (HLA)-II haplotypes (often DQ2 or DQ8) in addition to several non-HLA factors [9]. CeD is caused by many immune mechanisms. Fundamentally, tissue transglutaminase 2 (TG2) has crucial implications for this immunopathology because it is known to operate as a target self-antigen in the immune response and as a deamidating enzyme that amplifies the immunostimulatory impact of gluten [10]. HLA-DQ2/DQ8 presents CD4+ T cells with TG2-deamidated gluten peptides, which promote Th1 cells in the intestinal lamina propria [10]. These gluten-specific Th1 cells also release the inflammatory cytokines interleukin (IL)-21 and interferon-γ [11], which further exacerbate inflammation. It has been suggested that environmental stressors lead to IL-15 overexpression, epithelial stress, and ultimately a loss of oral tolerance to gluten [12]. It is believed that this adaptive response increases tissue stress and opens the door for cytotoxic CD8+ T cells to lyse epithelial cells. As a result, the mucosa changes over time, as indicated by lymphocyte infiltration, hyperplasia, and atrophy [12].
After consuming gluten, CeD patients have severe gastrointestinal symptoms such as bloating, abdominal discomfort, and diarrhea [13]. Other manifestations of the disease include malabsorption and anemia, which are consequences of mucosal injury. The genetic etiology of CeD has been established thus far, as 90% of CeD patients have the HLA-DQ2 antigen, which is linked to a greater T helper cell response specific to gluten [9]. The remaining patients had HLA-DQ8, the second antigen. The HLA-DQ2 or HLA-DQ8 allele plays a crucial role in the development of CeD. However, despite possessing these risk genes, 30%–40% of the general population does not show any symptoms when exposed to dietary gluten, according to multiple follow-up population genetics and in vitro functional studies [14,15]. This also highlights the possible molecular interactions between risk alleles for HAs and non-HLAs, genetic expression, and epigenetic modifications [11,16]. All of these factors might initiate a series of immunological reactions that are essential for the development of CeD [17].
Dysbiosis of normal gut microbiota has been linked to a number of gastrointestinal illnesses, including CeD [18]. The gut microbiota composition of CeD patients differs from that of healthy controls [18,19]. The majority of research indicates that fecal and duodenal samples from patients with CeD exhibit dysbiosis, which is defined by a decrease in the ratio of Bifidobacterium spp./Bacteroides and Enterobacteriaceae as well as a decrease in the ratio of Firmicutes/Bacteroidetes [20]. Moreover, dysbiosis was identified prior to the onset of CeD and was characterized by a decrease in butyrate-producing and anti-inflammatory species and an increase in proinflammatory bacteria [21]. These findings led to the conclusion that microbiota composition influences the pathophysiology of CeD. The microbiome has therefore become a critical focus for potential therapy.
A gluten-free diet (GFD) is currently the cornerstone of the CeD treatment plan. However, many individuals with CeD do not see clinical improvement even when strictly following a GFD [22]. To improve the treatment of CeD, it is important to investigate additional methods in addition to the GFD. Probiotics with a variety of bacterial strains have recently been suggested as adjuvant treatments when used in conjunction with GFD. Recent research has shown that probiotic use is effective in decreasing the intensity of GI symptoms and may alter the composition of the intestinal microbiota in people with CeD [23]. Probiotic use has been suggested to help restore a normal percentage of beneficial bacteria and address imbalances in the gut microbiota of the gastrointestinal tract [24]. In addition, the considerable peptidolytic and proteolytic activity of both Lactobacillus fermentum and Bifidobacterium lactis enhances gluten-degrading capacities in vitro [25,26]. Recent research suggests that probiotics may have an impact on both the innate and adaptive immune systems. For example, research on patient cohorts revealed that reduced synthesis of the proinflammatory cytokine TNF-α was associated with an increased Firmicutes/Bacteroidetes ratio [27]. In another trial, probiotic therapy resulted in a decrease in the median IgA-tTG ratio [28]. Probiotic strains belonging to the Lactobacillus and Bifidobacterium genera are clinically approved. In both clinical and preclinical trials in mice, treatment with these probiotics modulated the number of B lymphocytes and/or immunoglobulin in the peripheral blood [29]. Similarly, an additional study demonstrated that the peripheral immune response was altered in a cohort trial involving children with CeD [30].
Research on the mechanisms behind the health advantages of probiotics remains scarce. Autophagy (ATG) is one of the main mechanisms that is expected to be involved in this process. Its role in autoimmune illness and gastrointestinal pathology has been thoroughly researched. This includes studies on the balance of gut microbiota, host defense against intestinal pathogens, and innate and adaptive immunity. There are three distinct mechanisms of ATG in mammals: chaperone-mediated ATG, microATG, and macroATG. This highly conserved process moves a variety of cell constituents, such as major aggregates, proteins, lipids, and organelles. It then combines with a lysosome to form an autolysosome or an autophagosome, which releases endogenous agents and breaks down the contents of the vesicle [31]. ATG activity can increase quickly when intracellular components are broken down and recruited to maintain energy homeostasis in response to dietary issues and other cellular challenges. The cellular process is mediated by a group of proteins related to ATG that coordinate the production of an autophagosome [32]. Among these proteins, the Class IIIPI3K/BECLIN1 complex is involved in the initiation step. However, the ATG16L1–ATG5–ATG12 complex forms the core molecular machinery of ATG. ATG3 and ATG7 are further recruited and involved in autophagosome maturation. The microtubule-associated protein 1A/1B-light chain 3 comprises the ATG8 family, which is involved in lipid conjugation and is crucial for cargo recruitment. These genes are key regulators of ATG pathways and are commonly used to detect ATG and further reflect the metabolic function of the tissue [33].
According to recent studies, ATG is an important mechanism through which probiotics promote and maintain gut homeostasis. In an intestinal organoid model generated from healthy and CeD patients, Furone et al. demonstrated that gliadin peptides might disrupt ATG, whereas probiotic or postbiotic pretreatment might prevent this disruption [34]. A study by Han et al. reported that ATG mediated by probiotics may be involved in protection against LPS-induced intestinal epithelial toxicity in cultured rat intestinal epithelial cells (IECs) [35]. Probiotics may promote macrophage function, as shown by increased expression of certain mediators, such as IL-6, TNF-α, and NO4, in cell line models [36–38]. Since macrophages are essential for the activation of the innate immune system, the ability of probiotics to promote macrophage ATG is likely the cause of this effect. Nonetheless, several investigations have also demonstrated the detrimental regulatory role of ATG in innate immune signaling pathways. For example, ATG can be triggered in response to stimulation of innate immune receptors (such as TLRs) to reduce the expression of proinflammatory cytokines [39]. Prebiotic supplements have also been demonstrated to restrict ATG in the context of allergic asthma, hence inhibiting inflammation and lowering oxidative stress levels in a rat model [19].
A few studies have been conducted on the function of probiotics in human ATG mediation, specifically on how probiotics affect blood ATG in the context of CeD. The present study aimed to investigate changes in the expression of key ATG-related proteins, atg7, atg16l1, atg12, lc3, and beclin1, caused by clinically validated oral-supplemented probiotics. We also explored what relevant symptom alterations arise as a result of probiotic supplementation in addition to changes in blood cell counts.
2. MATERIALS AND METHODS
2.1. Study design and cohort establishment
This randomized, controlled trial was conducted in two places inside Jordan that were used for the study: Aqaba, located in the southwest, and Amman city, located in the center. Confirmed cases of CeD (n = 25 probiotic treatment) and controls (n = 13 no probiotic treatment) were included in this investigation from September 2023 to December 2023. All study participants provided written informed consent prior to the research, and the ethical review committee of Al-Balqa Applied University authorized all the studies under approval number 26/3/1/1759. The collaboration of the Life Society for CeD Patients in Aqaba and the Celiac Care Providers Society in Amman allowed for the formation of the cohort. At both locations, society establishes connections with patients and gives registered patients access to contacts. The participant eligibility requirements included (a) being between the ages of 18 and 65 years and (b) having a prior positive diagnosis according to the current guidelines for CeD diagnosis [40], including a positive tTG autoantibody level above the cutoff normal (normal < 18 U/ml) and a history of positive small bowel biopsy results and serologic markers for CeD. (c) A strict GFD for at least 1 year before the study. The exclusion criteria were as follows: clinically important conditions related to the heart, lungs, kidneys, hematologic system, nervous system, or psyche; a history of gastrointestinal cancer and/or surgery; pregnancy or lactation; and current antimicrobial treatment usage at least 1 month prior to the study. The demographic data of the participants are presented in Table 1. We used Google Forms to develop a digital survey with a tailored questionnaire that collected information about antibiotic intake during the previous month, smoking status, physical activity, diet, chronic disease, supplement intake, and the latest Anti-Ttg results, and covered all necessary participation conditions. After sifting through the 250+ responses, we arrive at the desired results. Figure 1 represents the study protocol. Despite having a high response rate from the beginning, the majority of respondents either stopped participating in the study or were atypical.
 | Figure 1. Schematic representation of the study protocol. [Click here to view] |
Table 1. Participant characteristics.
| PT ID | Sex (M/F) | Age | Smoker/nonsmoker | GFD > 12 months | Antibiotic intake | Anti-Ttg test |
|---|---|---|---|---|---|---|
| Pt1 | M | 59 | Nonsmoker | Yes | No | >18 U/ml |
| Pt2 | F | 42 | Nonsmoker | Yes | No | >18 U/ml |
| Pt3 | F | 39 | Nonsmoker | Yes | No | >18 U/ml |
| Pt4 | M | 25 | Nonsmoker | Yes | No | >18 U/ml |
| Pt5 | F | 43 | Nonsmoker | Yes | No | >18 U/ml |
| Pt6 | M | 45 | Smoker | Yes | No | >18 U/ml |
| Pt7 | F | 38 | Nonsmoker | Yes | No | >18 U/ml |
| Pt8 | F | 43 | Nonsmoker | Yes | No | >18 U/ml |
| Pt9 | F | 30 | Nonsmoker | Yes | No | >18 U/ml |
| Pt10 | F | 24 | Nonsmoker | Yes | No | >18 U/ml |
| Pt11 | F | 20 | Nonsmoker | Yes | No | >18 U/ml |
| Pt12 | F | 41 | Nonsmoker | Yes | No | >18 U/ml |
| Pt13 | F | 34 | Nonsmoker | Yes | No | >18 U/ml |
| Pt14 | F | 60 | Nonsmoker | Yes | No | >18 U/ml |
| Pt15 | F | 46 | Nonsmoker | Yes | No | >18 U/ml |
| Pt16 | F | 49 | Nonsmoker | Yes | No | >18 U/ml |
| Pt17 | F | 45 | Nonsmoker | Yes | No | >18 U/ml |
| Pt18 | M | 19 | Nonsmoker | Yes | No | >18 U/ml |
| Pt19 | M | 18 | Nonsmoker | Yes | No | >18 U/ml |
| P20 | M | 42 | Nonsmoker | Yes | No | >18 U/ml |
| Pt21 | F | 51 | Nonsmoker | Yes | No | >18 U/ml |
| Pt22 | F | 51 | Nonsmoker | Yes | No | >18 U/ml |
| Pt23 | M | 44 | Nonsmoker | Yes | No | >18 U/ml |
| Pt24 | M | 40 | Nonsmoker | Yes | No | >18 U/ml |
| Pt25 | F | 50 | Nonsmoker | Yes | No | >18 U/ml |
| Pt26 | F | 47 | Nonsmoker | Yes | No | >18 U/ml |
| Pt27 | F | 39 | Nonsmoker | Yes | No | >18 U/ml |
| Pt28 | F | 21 | Nonsmoker | Yes | No | >18 U/ml |
| Pt29 | M | 35 | Smoker | Yes | No | >18 U/ml |
| Pt30 | M | 52 | Smoker | Yes | No | >18 U/ml |
| Pt31 | F | 37 | Nonsmoker | Yes | No | >18 U/ml |
| Pt32 | F | 38 | Nonsmoker | Yes | No | >18 U/ml |
| Pt33 | F | 35 | Nonsmoker | Yes | No | >18 U/ml |
| Pt34 | F | 21 | Nonsmoker | Yes | No | >18 U/ml |
| Pt35 | F | 58 | Nonsmoker | Yes | No | >18 U/ml |
| Pt36 | F | 34 | Nonsmoker | Yes | No | >18 U/ml |
| Pt37 | F | 18 | Nonsmoker | Yes | No | >18 U/ml |
| Pt38 | F | 40 | Smoker | Yes | No | >18 U/ml |
Anti-tTg = anti-tissue transglutaminase; F = Female; GFD = gluten-free diet; M = male; PT ID = patient identification.
The sample size determination was informed by previous research indicating a 20% reduction in gastrointestinal symptom severity [gastrointestinal symptom rating scale (GSRS) score] among participants in the probiotic group [24,41–44]. Using G*Power software, a two-group comparison was conducted with an 80% power and a 5% significance level (α = 0.05), which established that at least 32 participants were necessary. To accommodate an expected dropout rate of 15%–20%, the final enrollment target was adjusted to 38 participants to maintain adequate statistical power.
No changes to methods were made after trial commencement.
2.1.1. Measurement of gastrointestinal symptoms and fiber intake
The GSRS digital survey was created via Google Forms (https://forms.gle/kCRqAnC4uDnHHzpg9) to monitor GI symptoms before supplement ingestion [45]. In addition, each participant provided a 3-day food record to complete during the study period to ensure their adherence to the GFD and analyze their fiber intake.
2.1.2. Probiotics
For eight weeks, the patient group received a formulation containing probiotic microorganisms (Lactobacillus acidophilus, L. bulgaricus, L. salivarius, L. reuteri, Streptococcus thermophilus, and bifidobacterial spp.) (5 × 109 CFU/g) twice daily. The second group received nothing.
2.2. Blood collection and hematological analysis
Blood samples were taken twice for measuring ATG gene expression levels and the complete blood count once during enrollment and once following the intervention period. Using normal phlebotomy techniques, 5 ml of whole blood is collected via vein puncture and placed immediately into a BD Vacutainer® CPT™ Cell Preparation Tube with sodium heparin. This blood was then utilized for peripheral blood mononuclear cell (PBMC) isolation and CBC via a programmed analyzer (Mindray, China). The CBC test results, including red blood cell (RBC), hemoglobin, white blood cell (WBC), and platelet counts, were as follows.
2.2.1. Isolation of PBMC
PBMCs were isolated according to the method described by Puleo et al. [46] with some adjustments. To ensure a homogenous suspension, blood was prepared by gently inverting several times. The blood was then centrifuged at 1,650 × g for 20 minutes at 20°C with shaking at room temperature. The plasma and PBMCs were pipetted into a sterile 50 ml conical tube, which was then filled with PBS. The conical tubes were centrifuged with the brake on for ten minutes at room temperature at a speed of 250× g. Using serological pipettes, the supernatant was gently aspirated after centrifugation to avoid disrupting the cell pellet. After the cells were carefully resuspended in 50 ml of PBS, they were centrifuged for 10 minutes at 250× g while the brake was applied. After being harvested, the PBMCs were separated into aliquots and chilled for two hours at 4°C. The samples were then deep frozen until RNA separation was achieved.
2.2.3. RNA, template synthesis, and reverse transcription-quantitative polymerase chain reaction
PBMC aliquots were used for RNA extraction via the TRI Reagent® (Zymo Research, USA). Briefly, 300 μl of each PBMC sample was mixed with 500 μl of TRI Reagent, and the rest of the protocol was followed as described by the manufacturer. The RNA concentration (ng) and purity (absorbance/ratio at 260/280, 260/230) were determined spectrophotometrically via the Nabi™ Ultraviolet-Visible Nano Spectrophotometer from MicroDigital, where pure RNA is defined as having a 260/280 absorbance ratio ranging between 1.7 and 2.0 and a 260/230 ratio ranging between 1.9 and 2.2. The integrity of the genomic RNA was determined by visualizing approximately 100 ng of RNA on a 1% agarose gel (w/v) containing 0.25 μg/μl ethidium bromide, and was run in 1X Tris-EDTA buffer at 100 V. The purified RNA (4 μl) was converted to cDNA via Takara PrimeScript™ RT Master Mix (Perfect Real Time, Japan) at 37°C for 15 minutes, followed by incubation at 85°C for 5 seconds at 4°C for 5 seconds. The reverse transcription reaction system included 4 μl of RNase-free dH2O, 2 μl of 5 × RT Buffer, 2 μl of RNase inhibitor, 1.2 μl of oligo dT primers (50 μM), 1.2 μl of random 6-mers (100 μM), 1.2 μl of NTP, 1.2 μl of RNA, and 4 μl of RNA. All primers were designed by Integrated DNA Technologies, Inc. (Table 2).
Table 2. Primers for RT-qPCR.
| Gene | Primer |
|---|---|
| beclin1 | F: 5'-CTCCATTACTTACCACAGCC-3' R: 5'-CAATAAATGGCTCCTCTCCTG-3' |
| atg7 | F: 5'-GTTGACCCAGAAGAAGCTG-3' R: 5'-CAGAGTCACCATTGTAGTAATAACC-3' |
| atg12 | F: 5'-CTTCAATTGCTGCTGGAGG-3' R: 5'-GGAGCAAAGGACTGATTCAC-3' |
| atg16l1 | F: 5'- ATCTTTGGGAGACGCTCTG-3' R: 5'-CACTTCTTTACCAGAACCAGG-3' |
| lc3 | F: 5'-CCACACCCAAAGTCCTCACT-3' R: 5'-CACTGCTGCTTTCCGTAACA-3' |
The RT-qPCR amplification reaction was carried out on a qTOWER³-type real-time PCR machine (Analytik Jena, Germany). Takara PrimeScript™ RT Master Mix (Perfect Real Time) was used to prepare the RT reaction, which included 4.5 μl of free nuclease dH2O, 12.5 μl of TB Green Master mix, 0.5 μl of forward primer (10 pmol/μl), 0.5 μl of reverse primer (10 pmol/μl), and 2 μl of cDNA (30 ng/Ul concentration). The amplification cycles consisted of 95°C for 30 seconds, followed by 95°C for 10 seconds (denaturation) and 62°C for 1 minute (annealing and extension).
2.3. Correction for amplification efficiency
To analyze the changes in ATG gene expression in probiotic-treated patients relative to controls, we used the method described by Pfaffl et al. [47]. The expression levels of the ATG-related genes were normalized to the levels of the housekeeping gene β-actin, which was used as an endogenous reference. A standard curve including a 1:4 diluted mixture of representative samples (n = 12) with four serial dilutions was generated for each of the primer pairs used in the amplification. This enabled the calculation of efficiency (E), which is used to determine the quality of amplification of each of the references and targeted genes in question. E values > 80% were accepted.
2.4. Normalization of RT-qPCR data
The normalization procedure was performed to account for variation in template loading across samples. Typically, this step includes the division of inferred target amounts by the abundance of reference genes. In this way, all the target abundances are expressed as fold differences relative to the abundance of the reference gene.
To achieve more accurate normalization, we used multiple nearly stable reference genes, as proposed by Vandesompele et al. [48]. In this case, the targeted mRNA abundances were divided by the geometric average of the reference gene abundances. The efficiency-corrected expression values (Q) for all the samples were subsequently calculated from the generated Cq values (Q = EddCT), where E is the amplification efficiency. The mean Cq values of technical duplicates were used for the calculations.
In this study, we started by evaluating a panel of reference genes and then selecting the most stable genes. We assessed four candidate reference genes of humans, namely, b-actin, gapdh, hprt1, and b2m, among those described in the literature. A total of four primer pairs (Table 3) for these four genes were evaluated via cDNA synthesized from 16 randomly selected cDNAs. The analysis of gene stability was performed via qPCR software implemented in qTOWER³ Analytik Jena. b-actin and gapdh were the most stable reference genes for this study.
Table 3. Primers for housekeeping genes.
| Gene | Primer |
|---|---|
| Hypoxanthine phosphoribosyl transferase 1(hprt1) | F: 5'-TGACACTGGCAAAACAATGCA-3' |
| R: 5'-GGTCCTTTTCACCAGCAAGCT-3' | |
| β-2-microglobulin (b2m) | F: 5'-GAGTATGCCTGCCGTGTGAAC-3' |
| R: 5'-CCAATCCAAATGCGGCATCTTC-3' | |
| Glyceraldehyde-3-phosphate dehydrogenase (gapdh) | F: 5'-GTCTCCTCTGACTTCAACAGCG-3' |
| R: 5'-ACCACCCTGTTGCTGTAGCCAA-3' | |
| Beta-actin (b-actin) | F: 5'-CCACCATGTACCCAGGCATT-3' |
| R: 5'-CGGACTCATCGTACTCCTGC-3' |
For all reference genes, normalization factors (NFs) were calculated via the geometric mean (GM) of the corresponding Q values (Qr) [44], which is exemplarily shown for the sample (x) as follows:
x = GM = √ (Beta actin) x * (gapdh)x. (Eq . 1)
We then calculated normalized expression values (NE) for all samples of our genes of interest to perform subsequent statistical analysis. All the calculated Q values were divided by the NF of each sample.
x = x−1 Nx. (Eq. 2)
3. STATISTICS
Paired t-tests were used to analyze changes in blood cell counts and ATG gene expression levels in 25 CeD patients before and after probiotic intervention. This parametric test was selected to evaluate whether mean differences in these continuous variables were statistically significant, providing insight into probiotic effects. For non-normally distributed data (assessed via Shapiro-Wilk tests) and ordinal symptom severity scores, the Wilcoxon signed-rank test was employed as a non-parametric alternative.
The analysis included:
- Normality testing (Shapiro-Wilk, α = 0.05) to guide test selection;
- Paired t-tests for parametric comparisons of blood/gene expression data;
- Wilcoxon tests for symptom scores and non-normal distributions;
- Effect size calculations (Cohen’s d for t-tests, rank-biserial for Wilcoxon); and
- Post-hoc power analysis to confirm detection capability.
This dual approach ensured appropriate methodology for both continuous laboratory measures (t-tests) and ranked symptom data (Wilcoxon), while maintaining statistical rigor through effect size quantification and power validation.
4. RESULTS
4.1 Descriptive statistics
The final number of CeD patients who were enrolled in this study was 38, although the initial number was 250. There were only 58 eligible candidates. Some patients dropped out because of antibiotic (n = 3) usage, and another 17 dropped out because of noncompliance. The patients were aged between 18 and 65 years [mean (SD) age = 37.8 ± 11.4 years], 10 patients (26%) were male, and 28 (74%) were female. There were no significant differences in age or sex between the subjects of the study. The participants were randomly assigned to the probiotic or control group. A detailed participant flow diagram is provided in Figure 2.
 | Figure 2. Participant flow diagram. [Click here to view] |
All patients have strict adherence to a GFD, which was achieved by both adherence surveys that all the participants undergo at the end of the study and food records during the study.
4.2. ATG gene expression levels and blood cell counts
The ATG gene expression levels and blood cell count were evaluated using Wilcoxon’s and the t-test. We concentrated on examining variations in blood measurements after eight weeks to draw a thorough judgment regarding the possible impact of probiotic consumption. Patients in the probiotics group significant decrease in atg7 and atg16l1 (see Fig. 3). We obtained a medium-sized effect using Cohen’s d statistic for these genes.
 | Figure 3. Autophagy gene expression profiles in CeD patient blood before and after probiotic consumption. Annotated box-and-whisker graphic of the blood expression levels derived from RT-qPCR. The relative quantitative expression (RQ) values are shown on the Y axes. The far-out values are shown as squares, and the outliers are shown as circles. On the other hand, ATG7 presented the greatest variability in its expression profile in blood samples (A). [Click here to view] |
The t-test has good power to test for a mean difference in gene expression and blood parameters, except for atg7, that don’t meet the normal distribution requirement (see Table 4). The normalized expression levels of lc3, beclin1, and atg12 did not show statistically significant changes following the intervention. While lc3 was tested using the t-test, beclin1, and atg12 were evaluated using the Wilcoxon test due to non-normality in their distributions.
Table 4. The results of the statistical analysis for gene expression levels and blood parameters for CeD patients who took probiotic (a combination of S. thermophiles, Bifidobacteria, and Lactobacillus strains). The results were considered significant when the p value was <0.05.
| Variable | Recommended test | Normal distribution | t test P | t test Sig | t test power | Wilcoxon P | Wilcoxon Sig | Wilcoxon Power | Cohens_D | Cohens_D_Interpretation | Rank_Biserial_r | Agreement |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| atg7 | Wilcoxon | No | 0.0116 | Yes | 0.746 | 0.149 | No | 0.934 | 0.547 | Medium | 0.694 | No |
| atg16l1 | T-test | Yes | 0.0051 | Yes | 0.841 | 0.0094 | Yes | 0.979 | 0.617 | Medium | 0.797 | Yes |
| lc3 | T-test | Yes | 0.3263 | No | 0.161 | 0.3752 | No | 0.887 | 0.2 | Small | 0.634 | Yes |
| atg12 | Wilcoxon | No | 0.1866 | No | 0.257 | 0.1616 | No | 0.91 | 0.272 | Small | 0.66 | Yes |
| beclin | Wilcoxon | No | 0.8943 | No | 0.052 | 0.8433 | No | 0.845 | 0.027 | Negligible | 0.595 | Yes |
| WBCs count (× 109/l) | T-test | Yes | 0.0829 | No | 0.412 | 0.0803 | No | 0.938 | −0.362 | Small | 0.7 | Yes |
| Neutrophils | T-test | Yes | 0.0053 | Yes | 0.836 | 0.0047 | Yes | 0.984 | −0.613 | Medium | 0.823 | Yes |
| Lymphocytes | T-test | Yes | 0.0594 | No | 0.476 | 0.0693 | No | 0.943 | −0.396 | Small | 0.708 | Yes |
| Monocytes | T-test | Yes | 0.0001 | Yes | 0.995 | 0.0005 | Yes | 0.994 | 0.953 | Large | 0.897 | Yes |
| Eosinophils | T-test | Yes | 0.0193 | Yes | 0.672 | 0.0221 | Yes | 0.968 | −0.502 | Medium | 0.762 | Yes |
| Basophils | Wilcoxon | No | 0.0288 | Yes | 0.607 | 0.0177 | Yes | 0.997 | −0.465 | Small | 0.943 | Yes |
| Hb (g/dl) | Wilcoxon | No | 0.8686 | No | 0.053 | 0.3981 | No | 0.932 | 0.033 | Negligible | 0.691 | Yes |
| RBCs (× 1012/l) | Wilcoxon | No | 0.4993 | No | 0.101 | 0.0408 | Yes | 0.956 | 0.137 | Negligible | 0.734 | No |
For atg7, which violated normality assumptions (Shapiro p < 0.05), the Wilcoxon test was recommended. While the t-test detected a significant reduction in atg7 expression (p = 0.0116), the Wilcoxon test did not (p = 0.149), despite its higher post-hoc power (93.4% vs. 74.6% for the t-test). This discrepancy reflects the Wilcoxon test’s conservative handling of non-normal data and its reduced sensitivity to outliers, rather than a failure to control false positives. Notably, atg7 exhibited the greatest variability among all targets (Fig. 3), which may further explain the divergent results. As shown in Table 4, the Wilcoxon test generally maintains valid false-positive rates but can be less powerful than the t-test when normality holds.
The leukocyte count changed significantly after the 8-week period (see Table 4). Specifically, neutrophils, eosinophils, basophils, and monocytes. Cohen’s d statistics indicate a medium to large effect on these measurements. There was no discernible difference in the levels of Hb and RBCs. Only the monocyte counts significantly increased, whereas all other measures significantly decreased. Taken together, these findings demonstrate that probiotics have a beneficial effect on the peripheral immune response and potentially lessen inflammatory responses, which may be particularly crucial for the treatment of CeD.
An additional detailed analysis is provided in the Supplementary Table 1.
4.3. Effects of oral probiotic supplementation on the symptoms of CeD
Changes in symptom severity scores before and after probiotic administration were evaluated in this study. Table 5 displays the findings from the Wilcoxon signed-rank test for different symptom severity levels.
Table 5. Wilcoxon signed-rank test results for symptom severity scores before and after probiotic intervention in individuals with CeD (significance level at p value < 0.05).
| Variable | Type | N | Recommended test | Normal distribution | t test P | t test Sig | t test power | Wilcoxon P | Wilcoxon Sig | Wilcoxon Power | Cohens_D | Cohens_D_Interpretation | Rank_Biserial_r | Agreement |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Discomfort | Symptom | 25 | Wilcoxon | No | 0.0028 | Yes | 0.892 | 0.0053 | Yes | 0.997 | 0.666 | Medium | 0.945 | Yes |
| Heartburn | Symptom | 25 | Wilcoxon | No | 0.0769 | No | 0.426 | 0.1316 | No | 0.998 | 0.37 | Small | 0.977 | Yes |
| Acid reflux | Symptom | 25 | Wilcoxon | No | 0.0388 | Yes | 0.555 | 0.0535 | No | 0.994 | 0.437 | Small | 0.9 | No |
| Hunger pain | Symptom | 25 | Wilcoxon | No | 0.7136 | No | 0.065 | 0.7055 | No | 0.998 | −0.074 | Negligible | 0.963 | Yes |
| Nausea | Symptom | 25 | Wilcoxon | No | 0.0018 | Yes | 0.921 | 0.0044 | Yes | 0.999 | 0.703 | Medium | 0.991 | Yes |
| Rumbling | Symptom | 25 | Wilcoxon | No | 0.0951 | No | 0.385 | 0.1235 | No | 0.997 | 0.347 | Small | 0.94 | Yes |
| Bloated | Symptom | 25 | Wilcoxon | No | 0.0007 | Yes | 0.962 | 0.0017 | Yes | 0.998 | 0.779 | Medium | 0.975 | Yes |
| Passing gas or flouts | Symptom | 25 | Wilcoxon | No | 0.0152 | Yes | 0.708 | 0.017 | Yes | 0.998 | 0.523 | Medium | 0.963 | Yes |
| Constipation | Symptom | 25 | Wilcoxon | No | 0.0494 | Yes | 0.511 | 0.086 | No | 0.995 | 0.414 | Small | 0.908 | No |
| Diarrhea | Symptom | 25 | Wilcoxon | No | 0.0071 | Yes | 0.805 | 0.0099 | Yes | 0.999 | 0.588 | Medium | 0.992 | Yes |
| Loos stool | Symptom | 25 | Wilcoxon | No | 0.0238 | Yes | 0.639 | 0.0356 | Yes | 0.997 | 0.483 | Small | 0.951 | Yes |
| Hard stool | Symptom | 25 | Wilcoxon | No | 0.0187 | Yes | 0.677 | 0.0299 | Yes | 0.996 | 0.504 | Medium | 0.931 | Yes |
| Urgent need to have bowel movement | Symptom | 25 | Wilcoxon | No | 0.0227 | Yes | 0.647 | 0.0263 | Yes | 0.998 | 0.487 | Small | 0.957 | Yes |
| Sensation of not completely emptying the bowel | Symptom | 25 | Wilcoxon | No | 0.0047 | Yes | 0.847 | 0.0075 | Yes | 0.997 | 0.622 | Medium | 0.935 | Yes |
The overall score improved significantly after 8 weeks of probiotic consumption in terms of discomfort, nausea, bloating, flouts, diarrhea, loose feces and hard stool, urgent need for bowel movement, and feeling as though the colon was not entirely empty (p < 0.05). The beneficial effects of probiotics on gastrointestinal symptoms were also observed, but not significantly, as a decline in constipation and acid reflux. Importantly, these findings lend credence to the beneficial effects of probiotics on gastrointestinal symptoms in patients with CeD. A better intestinal function combined with a stronger pharmaceutical therapeutic impact could indeed result in this beneficial outcome.
4.4. Control group measurements
Patients in the control group revealed no conclusive evidence of changes in any of the blood parameters or gene expression levels, as indicated by the high p-values (See Table 6). In addition, the results of the Wilcoxon signed-rank test indicate that there was no discernible change in the level of several gastrointestinal symptoms in the control group during the course of the study. Discomfort, heartburn, acid reflux, nausea, bloating, constipation, diarrhea, and urgency were among the symptoms that showed no change. The p-values in Table 7 indicate that the only discernible trend was in hunger pains, which approached but fell short of significance. An additional detailed analysis of the control group is provided in the Supplementary Table 2.
Table 6. The results of the statistical analysis for gene expression levels and blood parameters of CeD patients who were not taking probiotics during the study period. The results were considered significant at a p value < 0.05.
| Variable | Type | N | Recommended test | Normal distribution | t test P | t test Sig | t test power | Wilcoxon P | Wilcoxon Sig | Wilcoxon power | Cohens_D | Cohens_D_Interpretation | Rank_Biserial_r | Agreement |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| atg7 | Numeric | 13 | T-test | Yes | 0.2312 | No | 0.213 | 0.2783 | No | 0.803 | −0.35 | Small | 0.78 | Yes |
| atg16l1 | Numeric | 13 | T-test | Yes | 0.2915 | No | 0.174 | 0.5879 | No | 0.571 | 0.306 | Small | 0.593 | Yes |
| lc3 | Numeric | 13 | T-test | Yes | 0.053 | No | 0.505 | 0.0681 | No | 0.814 | 0.595 | Medium | 0.791 | Yes |
| atg12 | Numeric | 13 | Wilcoxon | No | 0.0254 | Yes | 0.649 | 0.0046 | Yes | 0.914 | 0.707 | Medium | 0.923 | Yes |
| beclin | Numeric | 13 | Wilcoxon | No | 0.8707 | No | 0.053 | 0.9697 | No | 0.556 | 0.046 | Negligible | 0.582 | Yes |
| WBCs count (x 10^9/L) | Numeric | 13 | T-test | Yes | 0.6094 | No | 0.077 | 0.7732 | No | 0.508 | 0.145 | Negligible | 0.549 | Yes |
| Neutrophils | Numeric | 13 | T-test | Yes | 0.5015 | No | 0.098 | 0.4548 | No | 0.617 | 0.192 | Negligible | 0.626 | Yes |
| Lymphocytes | Numeric | 13 | T-test | Yes | 0.4997 | No | 0.098 | 0.3137 | No | 0.669 | −0.193 | Negligible | 0.665 | Yes |
| Monocytes | Numeric | 13 | T-test | Yes | 0.8073 | No | 0.056 | 0.946 | No | 0.461 | 0.069 | Negligible | 0.516 | Yes |
| Eosinophils | Numeric | 13 | T-test | Yes | 0.2549 | No | 0.196 | 0.21 | No | 0.775 | 0.332 | Small | 0.753 | Yes |
| Basophils | Numeric | 13 | T-test | Yes | 0.8372 | No | 0.054 | 0.8975 | No | 0.571 | 0.058 | Negligible | 0.593 | Yes |
| Hb (g/dl) | Numeric | 13 | T-test | Yes | 0.1044 | No | 0.366 | 0.0957 | No | 0.83 | 0.487 | Small | 0.808 | Yes |
| RBCs (× 1012/l) | Numeric | 13 | T-test | Yes | 0.5273 | No | 0.092 | 0.7495 | No | 0.61 | 0.181 | Negligible | 0.621 | Yes |
Table 7. The results of the statistical analysis of various symptom severity scores in the control group (CeD patients who were not taking probiotics) during the study period. The results were considered significant at a p value < 0.05.
| Variable | Type | N | Recommended test | Normal distribution | t test P | t test Sig | t test power | Wilcoxon P | Wilcoxon Sig | Wilcoxon Power | Cohens_D | Cohens_D_Interpretation | Rank_Biserial_r | Agreement |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Discomfort | Symptom | 10 | Wilcoxon | Yes | No | 0.05 | 1 | No | 0.885 | 0 | Negligible | 1 | Yes | |
| Heartburn | Symptom | 13 | Wilcoxon | No | 0.337 | No | 0.152 | 1 | No | 0.95 | 0.277 | Small | 1 | Yes |
| Acid reflux | Symptom | 13 | Wilcoxon | No | 0.337 | No | 0.152 | 1 | No | 0.95 | 0.277 | Small | 1 | Yes |
| Hunger pain | Symptom | 12 | Wilcoxon | No | 0.1661 | No | 0.273 | 0.5 | No | 0.934 | 0.428 | Small | 1 | Yes |
| Nausea | Symptom | 12 | Wilcoxon | No | 0.5863 | No | 0.081 | 1 | No | 0.922 | 0.162 | Negligible | 0.974 | Yes |
| Rumbling | Symptom | 13 | Wilcoxon | No | 0.6727 | No | 0.068 | 1 | No | 0.946 | 0.12 | Negligible | 0.989 | Yes |
| Bloated | Symptom | 13 | Wilcoxon | Yes | No | 0.05 | 1 | No | 0.95 | 0 | Negligible | 1 | Yes | |
| Passing gas or flouts | Symptom | 12 | Wilcoxon | Yes | No | 0.05 | 1 | No | 0.934 | 0 | Negligible | 1 | Yes | |
| Constipation | Symptom | 13 | Wilcoxon | No | 0.337 | No | 0.152 | 1 | No | 0.95 | −0.277 | Small | 1 | Yes |
| Diarrhea | Symptom | 13 | Wilcoxon | Yes | No | 0.05 | 1 | No | 0.95 | 0 | Negligible | 1 | Yes | |
| Loos stool | Symptom | 13 | Wilcoxon | No | 0.337 | No | 0.152 | 1 | No | 0.95 | −0.277 | Small | 1 | Yes |
| Hard stool | Symptom | 13 | Wilcoxon | Yes | No | 0.05 | 1 | No | 0.95 | 0 | Negligible | 1 | Yes | |
| Urgent need to have bowel movement | Symptom | 13 | Wilcoxon | Yes | No | 0.05 | 1 | No | 0.95 | 0 | Negligible | 1 | Yes | |
| Sensation of not completely emptying the bowel | Symptom | 13 | Wilcoxon | No | 0.337 | No | 0.152 | 1 | No | 0.95 | −0.277 | Small | 1 | Yes |
Overall, our data imply that probiotic supplementation has a substantial influence on symptom severity in the CeD group, highlighting the need for more studies into the possible benefits of dietary supplementation in various groups of patients or under alternative situations.
4.5. Effects of dietary fiber intake during the study period
The 3-day meal records were made available to all participants who duly completed them. Although recommendations for sufficient dietary fiber intake vary by age group and according to nutritional standards, individuals are generally advised to consume 25–30 g or more per day. The analysis process involved counting the fiber content during each day of the 3-day recording, which was accomplished by counting the grams of fiber in each meal each day, using the USDA National Nutrient Database for Standard Reference Legacy (2018) and the Nutrient Total Dietary Fiber (g) table for counting and calculation. The results were as follows: approximately half of the participants met their daily intake needs, and half of them did not. Vegetables such as tomatoes and cucumbers were the main source of dietary fiber in the cohort under investigation, followed by fruits such as orange, bananas, and apples, with seeds and nuts contributing very little. The average amount of fiber intake was 30.95 g. The lowest fiber intake value was 8.15 g, and the greatest value was 87.13 g.
As shown in Table 8, we did not find enough evidence to conclude that the CeD cohort’s expression levels of the studied genes were impacted by the fiber dose during the study period, as the statistical analysis indicated.
Table 8. Results of the statistical analysis of the effects of fiber intake on the expression of autophagy markers
| Gene | Coefficient | P value | R2 | Interpretation |
|---|---|---|---|---|
| atg7 | −0.0115 | 0.191 | 0.073 | Slight decrease, not statistically significant |
| atg16l1 | −0.0030 | 0.175 | 0.079 | Minor decrease, not statistically significant |
| lc3 | 0.0010 | 0.398 | 0.031 | Slight increase, not statistically significant |
| beclin1 | 0.0007 | 0.828 | 0.002 | Negligible change, not statistically significant |
| atg12 | 0.0096 | 0.336 | 0.040 | Slight increase, not statistically significant |
This is demonstrated by:
1-A lack of statistical significance: For all genes analyzed, the p-values associated with fiber intake were greater than 0.05.
2-Low explanatory power: The R2 values are low across all the models, indicating that fiber intake does not meaningfully explain changes in gene expression.
3-Coefficients: While some coefficients are negative and others are positive, none are statistically significant, and their magnitudes are small.
No adverse events or unintended effects were reported in either group during the trial.
5. DISCUSSION
For conditions in which the gut mucosa is compromised, probiotics are potentially an anti-inflammatory treatment. Most probiotic research focuses on the use of probiotics to prevent and/or treat digestive disorders or other diseases linked to aberrant microbiota or irritated mucosa. Therefore, translational research often ignores the complicated underlying mechanisms of action of probiotics. According to previous findings, probiotic bacteria with proteolytic activity—Bifidobacterium, Lactobacillus, and S. thermophiles—may affect blood cell counts and the expression of genes related to blood ATG in patients with CeD. This may be a potential mechanism by which probiotics extend their effects to different tissues and cells.
We chose to employ a combination of S. thermophiles, Bifidobacteria, and Lactobacillus strains in this investigation. This approach is justifiable because combining probiotics can have more significant benefits for immune system stimulation and microbiota modification, in addition to enabling broader favorable effects on the host. Because preclinical and clinical studies have shown beneficial effects of S. thermophiles, Bifidobacteria, and Lactobacillus strains, they are frequently included in prebiotic formulations.
The study’s qualitative findings were centered on evaluating gastrointestinal symptoms. These results indicate that probiotic supplements can benefit people with CeD in terms of symptoms, particularly those related to gastrointestinal health. This finding has important implications because a strict GFD is still insufficient to fully relieve symptoms in many patients, and products designated for CeD sufferers may include some gluten. Moreover, a GFD places severe restrictions on one’s food intake. Thus, a considerable proportion of CeD patients require adjuvant therapy in addition to a GFD. Probiotic treatment has been shown to improve gastrointestinal symptoms in human studies [49]. Adult CeD patients who experienced symptoms even after adhering to a GFD for at least 2 years were evaluated by Francavilla et al. [50] for gastrointestinal symptoms when given a combination of Bifidobacterium and Lactobacillus. After 6 weeks of treatment, they reported a substantial decrease in the GSRS score in the probiotic group compared with the placebo group. Smecuol et al. [51], on the other hand, used only Bifidobacterium as a probiotic and reported no discernible modifications in the celiac symptom index (CSI) or GSRS between the probiotic and placebo groups. However, the probiotic group of the trial showed a much greater reduction in indigestion and constipation CSI than the placebo group did. Similarly, a trial on children with CeD reported a significantly greater reduction in stool frequency in the probiotic arm than in the placebo arm after four weeks of treatment [52].
This study demonstrated that a reduced WBC count and differential WBC count, with the exception of monocytes, compared with the baseline value before probiotic supplementation, may indicate that probiotic supplementation has an anti-inflammatory effect. Numerous studies on CeD have assessed the potential of probiotics to reduce inflammation via various methods, such as modulation of the immune response in the gut mucosa and periphery [30,52,53], effects on TNF-α [27,54,55], and the use of coeliac-specific markers [28,30,51]. None of the previous groups examined the impact of probiotics on WBCs or their differential effects. However, the results of this study were consistent with those of earlier studies that did not show a significant immunological increase, suggesting a possible protective effect of probiotics. Every study that evaluated the immunologic response via various approaches and markers revealed some degree of immunomodulation. We detected an increased monocyte count compared with that at baseline before the consumption of probiotics. Numerous studies have shown that probiotics promote the generation of monocyte/macrophage cytokines and have a direct effect on monocyte/macrophage phenotype and function both in vitro [38,56] and in vivo [57,58]. We hypothesized that the observed shift in WBCs and their differentials could be explained by the increase in the gut barrier activities of probiotics, which reduces bacterial translocation and leukocyte activation in the bloodstream. Furthermore, intricate interactions with other cell types—not only immune cells—likely occur in vivo and alter monocyte transcriptional profiles, which could alter how monocytes react to bacterial stimulation and could perhaps positively impact the capacity to combat infections. Although the relationships between probiotics, commensal bacteria, and host immunity are well established, the particular molecular mechanisms underlying this “bacteria-host crosstalk” have yet to be comprehensively defined. Contemporary investigations underscore the importance of quorum sensing, extracellular ATP, protein-protein interactions, and sophisticated proteomic methodologies in deciphering these intricate relationships [59,60]. These findings may facilitate the formulation of innovative therapeutic approaches and improve the effectiveness of probiotic interventions.
The impact of probiotics on ATG activation has been the main focus of this study employing RT-qPCR techniques, which examine gene expression at various phases of the ATG signaling pathway. Multiple ATG proteins work in concert to generate autophagosomes in multiple stages. We investigated Beclin1, as it is involved in controlling the initiation phase. Other ATG proteins, such as atg16l1, act as mediators of nucleation and cargo recruitment, whereas atg7 coordinates the elongation phase. Compared with the measurements made prior to probiotic treatment, the present study revealed significant variability in the gene expression of atg16l1 and its downstream gene atg7. Despite their small number, research has shown that probiotic therapy can impact ATG in CeD by altering ATG expression [34,61]. Importantly, atg16l1 and atg7 play significant roles in ATG activation via both the canonical and noncanonical pathways, which circumvent the function of proteins linked to ATG, such as lc3 [62]. Studies have shown that mutation or knockdown of atg16 or atg7 leads to decreased ATG, altered cytokine levels, and increased susceptibility to inflammatory disease [31,63]. Nevertheless, probiotic administration had little effect on the expression levels of lc3, beclin1, and atg12 in the PMBC. Similarly, some scholars have not reported a significant effect of probiotics on the expression levels of either the lc3 or beclin1 gene in placental tissue [65]. Conversely, Han et al. [35] reported decreased expression of lc3 upon probiotic treatment in intestinal tissue. Another study demonstrated that probiotics enhanced both atg16l1 and lc3 gene expression in IECs [63,64]. Together, these findings indicate that the ATG signaling pathway is crucial for maintaining tissue homeostasis in various organs. However, the context-dependent effect of ATG activation should be considered because the type of cell or disease state might affect whether an ATG-related gene is increased or inhibited. However, prior to this investigation, there was no information regarding the relationships between PBMC ATG, probiotic therapy, and CeD.
The use of genetic markers in clinical settings is still relatively new. Furthermore, most studies on CeD involve intestinal biopsies, which are more invasive and unpleasant than blood sampling. The current results show that PBMC ATG analysis can be employed for CeD and could be utilized to assess the prognosis of patients with CeD who are receiving probiotic treatment. However, a blood-based multi-ATG gene model needs to be developed.
The effect of dietary fiber intake on the expression of genes linked to ATG has not been fully characterized, although our findings imply that dietary fiber consumption does not significantly affect the chosen ATG biomarker within the existing ranges of consumption. This could also be explained by the relatively small sample size of the cohort, which could make it more difficult to identify minor effects. This limited dataset also restricted our ability to employ state-of-the-art machine learning techniques for further analysis [66,67]. Future research with more variables or larger sample sizes may shed more light.
6. CONCLUSION
The results of this study may contribute to the clinically evident systemic effects of probiotics on blood cells and ATG. Other immunological factors, as well as WBC differentiation on a quantitative time plot, should be analyzed in future well-designed studies that may elucidate the biological and immunological aspects of this entity and drive practical implications. In addition, the present study is among the first clinical studies that investigated the effects of probiotics on blood cells and ATG in CeD patients receiving a GFD, and data for making a practical assumption are scarce. Another limitation of this study was the small sample size, which made subgroup analysis inconclusive, especially for patients. Limitations in terms of study duration and difficulties in patient cooperation should be considered.
7. LIST OF ABBREVIATIONS
APCs, antigen presenting cells; ATG, autophagy; ATG16L, autophagy related 16 like 1; B2M, β-2-microglobulin; CeD, celiac disease; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GFD, gluten free diet; GI, gastrointestinal; GSRS, gastrointestinal symptom rating scale; HLA, human leukocyte antigen; HPRT1, hypoxanthine phosphoribosyl transferase 1; IL, interleukin; LC3B, microtubule-associated proteins 3; Th, T helper cell; TNF-a, tumor necrosis factor alpha; tTG2, transglutaminase type 2; β-actin, beta-actin.
8. ACKNOWLEDGMENTS
The authors would like to acknowledge the cooperation of Celiac Care Providers Society (Amman, Jordan) and the Life Society for Celiac Disease (Aqaba, Jordan) in our cohort establishment. The authors would like to thank One labs (Amman, Jordan) and Sultan Medical Labs Southern (Aqaba, Jordan) for facilitating the blood sample collection process.
9. AUTHOR CONTRIBUTIONS
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be an author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.
10. FINANCIAL SUPPORT
Funding was provided by the Al-Balqa Applied University-Deanship of Scientific Research (DSR), grant ID DSR-2023#555.
11. CONFLICTS OF INTEREST
The authors report no financial or any other conflicts of interest in this work.
12. ETHICAL APPROVALS
The experimental protocols were established following the Declaration of Helsinki and approved by the Ethics Committee of Al-Balqa Applied University (26/3/1/1759). All methods were carried out in accordance with relevant guidelines and regulations.
13. DATA AVAILABILITY
The data used in this article are openly available in the [Mendeley Data] at DOI:10.17632/h5bxzfp4vc.1
14. PUBLISHER’S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
16. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY
The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.
17. CONSENT TO PARTICIPATE
Consent to participate was collected before participation as verbal and written consent. The consent form will be provided upon request.
18. CONSENT FOR PUBLICATION
Consent for the use of individuals’ information without using individuals’ names was collected as written consent in addition to verbal consent for publication.
19. TRIAL REGISTRATION
This study was registered at the Open Science Framework with the registration DOI: https://doi.org/10.17605/OSF.IO/568B2.
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