Front. Pediatr.Frontiers in PediatricsFront. Pediatr.2296-2360Frontiers Media S.A.10.3389/fped.2025.1637284PediatricsSystematic ReviewA network meta-analysis of probiotics in the treatment of childhood asthmaSunJiajia1†ZhuMeiyi1†LiuYing2*ZhangDi2*Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, ChinaDepartment of Pediatrics, The Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
Edited by:Ke Chen, University of Electronic Science and Technology of China, China
Reviewed by:José J. Leija-Martínez, Autonomous University of San Luis Potosí, Mexico
Xin Sun, Air Force Medical University, China
Correspondence: Di Zhang zhangdi@jxutcm.edu.cn Ying Liu 595317906@qq.com
These authors have contributed equally to this work and share first authorship
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Objective
This network meta-analysis aims to explore the efficacy and safety of probiotics in children with asthma and attempts to determine which probiotics are most effective in improving outcomes in children with asthma by ranking methods.
Methods
A systematic search of Chinese and English databases, including China National Knowledge Infrastructure, Wanfang, VIP, PubMed, and Web of Science, was conducted from the establishment of the databases until July 2024 to screen for randomized controlled trials (RCTs) of probiotics in the treatment of childhood asthma. Lung function was used as the primary outcome measure, and secondary outcome measures included the total clinical response rate, recurrence rate, immune factors, cytokines, and Childhood Asthma Control Test (C-ACT) score. Data processing and analysis were performed using RevMan 5.4 and Stata 17.0 software.
Results
A total of 34 RCTs were included, involving 3,839 participants and 13 interventions. Our analysis showed that conventional treatment combined with probiotics improved outcome indicators in children with asthma better than conventional treatment. Conventional treatment combined with Bifidobacterium–Lactobacillus triplex live bacteria had the highest probability of being the optimal intervention in terms of increasing FEV1% and recurrence rate. Conventional treatment combined with Bifidobacterium adolescentis had the highest probability of being the optimal intervention in increasing FEV1. Conventional treatment combined with Lactobacillus tablets had the highest probability of being the optimal intervention in increasing peak expiratory flow. Conventional treatment combined with Bacillus subtilis diplex live bacteria had the highest probability of being the optimal intervention in improving the total clinical response rate. Conventional treatment combined with Bifidobacterium quadruplex live bacteria had the highest probability of being the optimal intervention in reducing IL-4 and IL-33. Conventional treatment combined with Bifidobacterium triplex live bacteria had the highest probability of being the optimal intervention in improving the C-ACT score.
Conclusion
Probiotics are effective in treating childhood asthma, and the therapeutic effect of conventional treatment combined with probiotics is superior to that of conventional treatment alone. Therefore, probiotics can be selected as appropriate in the clinical treatment of childhood asthma. However, the overall quality of the evidence was at most low or moderate, suggesting that the certainty of the evidence for probiotics in treating childhood asthma was low.
probioticsconventional treatmentnetwork meta-analysischildhoodasthma20224BAB216095Natural Science Foundation of Jiangxi, Chinasection-at-acceptancePediatric PulmonologyBackground
Asthma is a heterogeneous disease characterized by chronic airway inflammation and airway hyperresponsiveness. Clinically, it is characterized by recurrent episodes of wheezing, coughing, shortness of breath, and chest tightness, which often occur or worsen at night and/or in the early hours of the morning. The cumulative incidence rates of childhood asthma in 1990, 2000, and 2010 were 1.09%, 1.97%, and 3.02% (1) based on epidemiological investigations. While asthma hospitalizations and deaths have declined in some countries, asthma, especially childhood asthma, still imposes an unacceptable burden (2). Beta-agonists and leukotriene antagonists are commonly used in clinical practice to treat childhood asthma, but they are less effective in regulating immune function, so childhood asthma is prone to relapse (3) and eventually develops into adult asthma (4).
With further research on the gut microbiota, researchers have found that probiotics can improve lung function in children with asthma, increase the total clinical response rate, improve quality of life, and reduce the recurrence rate (5) of asthma. The types of probiotics involved in these reports on treating childhood asthma are diverse. Currently, there is a lack of comparative studies on the efficacy of different interventions, and there is no mention of probiotic types, specific usage, or treatment regimens (6) in the relevant guideline for childhood asthma. As a result, clinicians often rely on subjective experience when making treatment decisions. Therefore, this study uses a network meta-analysis method to compare the effects of different probiotic interventions on children’s lung function, clinical efficacy, and reduction of recurrence rate, to provide evidence-based medical support for optimizing drug selection in clinical practice.
Data and analysisInclusion and exclusion criteria
The inclusion and exclusion criteria for this study were based on the PICOS strategy. Only randomized controlled trials (RCTs) reported in English or Chinese were included, with no geographical restrictions. Non-RCTs, animal experiments, case studies, expert opinions, and other types of studies were excluded.
This study included children with asthma aged 5–18 years. The age range of 5–18 years was selected to ensure diagnostic accuracy, as lung function testing is challenging in younger children, and to maintain homogeneity across studies. Intervention measures in the experimental group included probiotics (Bifidobacterium triplex live bacteria, Bifidobacterium quadruplex live bacteria, Bifidobacterium–Lactobacillus triplex live bacteria, Bifidobacterium adolescentis, Saccharomyces boulardii, Bacillus subtilis diplex live bacteria, Lactobacillus tablets, LF(LactobacillusBCRC 910259), LP(LactobacillusGMNL-133)) combined with conventional treatment. The control group received conventional treatment [standard treatment for childhood asthma based on contemporary guidelines, mainly including but not limited to: inhaled glucocorticoids (ICS), leukotriene receptor antagonists, short-acting/long-acting beta-2 agonists (SABA/LABA), anticholinergic drugs] or conventional treatment combined with placebo.
The exclusion criteria included (1) in cases of duplicate publications, only the earliest article (by publication date) is included, and if the same manuscript has been submitted to multiple publications, only the version with the most complete data is selected; (2) non-RCT and RCT studies not involving children; (3) reviews, discussions, empirical cases, animal experiments, etc.; (4) literature with missing data or where full text cannot be obtained; and (5) literature involving participants with coexisting organic diseases or other diseases.
Database and retrieval strategy
The system searched Chinese and English databases, including China National Knowledge Infrastructure, Wanfang, VIP, PubMed, and Web of Science. The search period was from the establishment of the database to 18 July 2024. Search terms include “Asthma,” “Cough-Variant Asthma,” “bronchial asthma,” “child*,” “Adolescence,” “probiotics,” “Bifidobacterium,” “Yeast fungus,” and “Bacillus subtilis double viable bacteria.”
Data screening and quality assessment
Two researchers (JS, MZ) independently conducted the literature search and screening using Zotero according to the established relevant criteria. They cross-checked the results, and any discrepancies were discussed with a third researcher until a consensus was reached.
Two researchers (DZ, YL) used the modified Jadad scale (7) and the Cochrane risk of bias tool (8) to assess the risk of deviation and quality of the included RCTs, and any disagreement was resolved by discussing with a third researcher.
Outcome measures
The primary outcome measures were lung function, including the ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in 1 s to the projected value (FEV1%), forced expiratory volume in 1 s (FEV1), and peak expiratory flow (PEF). The secondary outcome measures were (1) clinical total effective rate, (2) recurrence rate, (3) adverse reactions, (4) immune factors (IgA and IgE), (5) cytokines (IL-4 and IL-33), and (6) Childhood Asthma Control Test (C-ACT) score.
Data analysis
The network structure was star-shaped, and there were no direct comparisons, so this study relied on the transitivity hypothesis. We assessed transitivity by examining the following: (1) patient characteristics, all studies included children aged 5–18 years with mild-to-moderate persistent asthma; (2) conventional treatment principles, while specific drug regimens varied (ICS monotherapy, ICS + LABA, montelukast), all followed stepwise management principles per contemporaneous guidelines; and (3) study methodology, all were RCTs with similar outcome assessment methods.
Outcome measures
For binary variables, relative risk (RR) was used as the effect size, whereas for continuous variables, standardized mean difference (SMD) was used as the effect size. All SMDs were based on endpoint values after intervention, and the effect size was expressed as a 95% confidence interval (95% CI).
Analysis method
A network plot was plotted using Stata 17.0. All RCTs including one multi-arm trial (9) were included and analyzed as single units using multivariate meta-analysis models without manual splitting. For effect size calculation, Hedges’ g and its standard error (SE) were used for SMD. The comparative relationship between studies is presented visually through a network diagram. Subgroup analysis and meta-regression were conducted at high heterogeneity (I2 > 50%). The therapeutic effects of the intervention were ranked by the surface under the cumulative ranking curve (SUCRA), and a SUCRA difference of >15% with non-overlapping CI was considered a significant difference. Funnel plots were plotted to assess the small sample effect. The adequacy of the sample size was evaluated through assessment of the precision of effect estimates and confidence interval widths.
ResultsLiterature search results
A total of 2,552 studies were retrieved from the database, and 478 duplicates were excluded. After screening for titles and abstracts, 109 records met the criteria for a full-text analysis. Among them, 72 studies were excluded by reading the full text. In total, 34 studies were included in this study (the literature screening process is shown in Figure 1).
Literature inclusion process.
Flowchart illustrating the identification of studies via databases and registers. Records identified are 2,552 from databases and zero from registers. After removing 478 duplicates and no ineligible records, 2,074 records were screened. Of these, 1,965 were excluded. From 109 reports sought for retrieval, three were not retrieved. Eligibility was assessed for 106 reports, with 72 excluded for reasons such as adult population, various diseases, ineligible intervention, not a randomized control trial, data error, and other reasons. Finally, 34 reports were included in the study.Basic information and quality assessment of the included literature
A total of 34 studies were included, 33 from a Chinese database and 1 from an English database. This study involved 3,839 participants and 13 interventions. The basic information of the included studies and the evaluation of literature quality (Jadad score) are presented in Table 1. The Cochrane risk of bias tool was used to evaluate the risk of deviation in the included studies (Figure 2). All the included studies were RCTs. Only one study discussed the use of allocation concealment and blinding, two studies discussed data dropout, the rest did not report data dropout or loss to follow-up, and six studies had small sample sizes and were at high risk of bias.
Basic characteristics of included literature as in original.
Inclusion of studies
Sample size (T/C)
Sex ratio (M/F)
Age (year)
Intervention
Treatments
Conclusion norm
Follow-up timing
Jadad rating
T
C
T
C
T
C
Sheng Ren 2020
42/42
25/17
24/18
9.15 ± 2.32
8.78 ± 1.74
1
12
4 weeks
EJH
6 months
2
Qibo Ma 2023
53/52
28/25
29/23
8.53 ± 1.77
8.47 ± 1.56
1
12
4 weeks
J
6 months
2
Rentao Wang 2012
78/78
No
No
No
No
1
12
12 weeks
EF
No
1
Dan Wang 2023
250/250
132/118
137/113
10.4 ± 2.9
10.9 ± 3.1
1
12
4 weeks
EJ
No
4
Chunhui Zang 2024
30/30
13/17
16/14
8.97 ± 1.00
9.10 ± 1.32
1
12
March
BEH
No
2
Yinfang Li 2023
31/31
16/15
15/16
9.9 ± 1.4
9.8 ± 1.6
1
12
6 months
ACDEHI
No
1
Qiaoxiang Xia 2022
46/46
24/22
21/25
6.84 ± 2.71
6.53 ± 2.59
1
12
4 weeks
CDE
No
2
Jia Li 2024
55/55
29/26
30/25
5.66 ± 1.25
5.62 ± 1.24
1
12
3 months
ABDEH
No
1
Jingfang Ma 2022
80/80
38/42
41/39
8.7 ± 1.2
8.8 ± 1.1
1
12
6 days
EJG
No
1
Bei Teng 2024
40/43
21/19
20/23
7.75 ± 1.82
7.75 ± 1.69
2
12
3 months
BH
3 months
2
Rongrong Tuo 2023
43/43
26/17
24/19
8.63 ± 1.52
8.22 ± 1.47
2
12
3 months
ACEJH
No
2
Shuxuan Chen 2021
55/55
24/31
21/34
7.71 ± 1.06
7.32 ± 1.21
2
12
1 months
I
No
2
Han Gao 2019
42/42
30/12
28/14
8.63 ± 1.73
8.70 ± 1.58
2
12
1 months
BCDGI
No
2
Chao Lu 2022
75/75
44/31
39/36
6.36 ± 4.54
6.89 ± 5.12
2
12
2 weeks
E
No
2
Deming Zhou 2020
50/50
25/25
24/26
9.24 ± 2.21
9.14 ± 2.22
2
12
No
BDFH
No
2
Lin Chen 2021
36/36
No
No
No
No
3
12
30 days
ACH
No
1
Shigang Li 2023
49/49
26/23
27/22
5.76 ± 1.42
5.46 ± 1.32
3
12
28 days
G
No
1
Qi Wang 2021
35/33
20/15
19/14
7.9 ± 1.9
7.9 ± 1.9
3
12
2 months
EI
3 months
3
Xue Li 2022
52/52
31/21
30/22
8.03 ± 1.26
7.85 ± 1.14
3
12
3 months
BCDEH
No
2
Shaoyan Wang 2012
30/30
No
No
No
No
3
12
3 months
EF
1 year
1
Yuwei Fu 2017
110/110
57/53
58/52
6.3 ± 0.6
6.1 ± 0.8
4
12
3 months
CDE
No
2
Yanfang Ma 2012
44/44
24/16
23/17
5.8 ± 1.1
5.6 ± 1.5
4
12
3 months
E
No
1
Linna Shen 2018
51/51
25/26
26/25
6.72 ± 2.38
6.38 ± 2.43
5
12
10 days
EG
No
1
Yan Yan Luo 2021
41/37
23/18
20/17
7.68 ± 3.91
7.70 ± 3.86
5
12
14 days
CE
No
2
Hongxia Dai 2023
55/55
32/13
30/25
5.90 ± 0.35
5.85 ± 0.30
5
12
14 days
EH
No
2
Weitu Chen 2020
54/54
33/21
29/25
Only the average age
Only the average age
6
12
6 weeks
ACDE
No
1
Bing Lv 2016
23/22
11/12
9/13
10.90 ± 2.10
10.10 ± 1.20
6
12
6 weeks
EGH
No
1
Wei Qin 2016
43/43
23/20
25/18
8.3 ± 3.3
9.8 ± 3.1
6
12
3 months
EF
half a year
1
Hui Li 2018
45/45
26/19
24/21
9.6 ± 3.2
8.8 ± 3.3
6
12
3 months
E
half a year
2
Yan Sun 2018
25/20
15/10
12/8
12.5 ± .2
12.4 ± 2.8
6
12
3 months
EF
No
1
Weiyu Xu 2023
52/52
30/22
31/21
7.18 ± 2.08
6.92 ± 1.79
7
12
4 weeks
EFG
June
3
Xiaofeng Yuan 2018
46/46
27/19
22/24
8.62 ± 1.73
8.48 ± 1.58
7
12
4 weeks
BDJH
No
2
Bingqi Zhang 2022
55/55
27/28
30/25
7.36 ± 2.16
7.86 ± 2.31
8
12
6 weeks
CDH
No
1
HuangChian-Feng 2018-1
38/35
22/16
18/17
7.68 ± 2.1
7.68 ± 2.50
9
13
3 months
GH
4 months
7
Huang Chian-Feng 2018-2
38/35
24/14
18/17
7.37 ± 2.34
7.68 ± 2.50
10
13
3 months
GH
4 months
7
Huang Chian-Feng 2018-3
36/35
19/17
18/17
7.00 ± 1.79
7.68 ± 2.50
11
13
3 months
GH
4 months
7
Intervention: (1) conventional treatment combined with Bifidobacterium triplex live bacteria; (2) conventional treatment combined with Bifidobacterium quadruplex live bacteria; (3) conventional treatment combined with Bifidobacterium–Lactobacillus triplex live bacteria; (4) conventional treatment combined with Bifidobacterium adolescentis; (5) conventional treatment combined with Saccharomyces boulardii; (6) conventional treatment combined with probiotics; (7) conventional treatment combined with Bacillus subtilis diplex live bacteria; (8) conventional treatment combined with Lactobacillus tablets; (9) conventional treatment combined with LF (LactobacillusBCRC 910259); (10) conventional treatment combined with LP (LactobacillusGMNL-133); (11) conventional treatment combined with LP+LF; (12) conventional treatment; (13) conventional treatment combined with placebo.
Bar chart showing risk of bias categories: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other bias. Green indicates low risk, yellow indicates unclear risk, and red indicates high risk. Most categories show a mix of green and yellow, with \"Other bias\" having a high risk at the end.Network meta-analysisNetwork diagrams of different interventions
The network diagram of conventional treatment combined with probiotics vs. conventional treatment is shown in Figure 3. Dots represent intervention measures, and arms represent comparative studies. The larger the dots, the larger the sample size included. The thicker the arms, the higher the contribution of the corresponding comparison studies (related to the number of references and sample size). Among them, the combination of conventional treatment and Bifidobacterium triplet live bacteria has the most direct comparisons with conventional treatment.
Network relationship diagram of each intervention, lung function, clinical effectiveness, recurrence rate, immune factors, cytokines, and C-ACT scores. The node “conventional treatment combined with probiotics” includes studies using probiotic supplements without specifying strain identity or commercial products. Each study was assigned to only one node based on intervention description clarity. (A) Each intervention; (B) FEV1/FVC; (C) FEV1%; (D) FEV1; (E) PEF; (F) total clinical effective rate; (G) recurrence rate; (H) IgA; (I) IgG; (J) IL-4; (K) IL-33; (L) C-ACT score. (1) Conventional treatment combined with Bifidobacterium triplex live bacteria; (2) conventional treatment combined with Bifidobacterium quadruplex live bacteria; (3) conventional treatment combined with Bifidobacterium–Lactobacillus triplex live bacteria; (4) conventional treatment combined with Bifidobacterium adolescentis; (5) conventional treatment combined with Saccharomyces boulardii; (6) conventional treatment combined with probiotics; (7) conventional treatment combined with Bacillus subtilis diplex live bacteria; (8) conventional treatment combined with Lactobacillus tablets; (9) conventional treatment combined with LF (LactobacillusBCRC 910259); (10) conventional treatment combined with LP (LactobacillusGMNL-133); (11) conventional treatment combined with LP+LF; (12) conventional treatment; (13) conventional treatment combined with placebo.
Twelve panels labeled A to L display stylized network diagrams with blue nodes and black connecting lines. Each diagram shows a central large node with several smaller nodes connected by lines of varying thickness and length, illustrating different connection patterns.Inconsistency test
Due to the open network structure without closed loops, inconsistency testing was neither feasible nor necessary. We employed a consistency model throughout, assuming agreement between direct and indirect evidence.
Five studies (10–14) were included in the analysis for the pooled estimation analysis of FEV1/FVC, and the network diagram (Figure 3) involved four interventions involving a total of 438 participants. Funnel plots were plotted, and Egger’s test was performed to assess publication bias for the metric. The funnel plots were visually asymmetrical, and the Egger’s test was performed with a P-value of 0.049, which is statistically significant but close to the critical value, suggesting the possibility of potential publication bias, considering the small sample effect and the higher possibility of publication bias in combination. The heterogeneity test for the forest plot (Figure 4) showed I2 = 97%, P = 0.000, suggesting high heterogeneity among the studies. Meta-regression and subgroup analyses were conducted on the included studies, and heterogeneity did not decrease. It is speculated that the sources of heterogeneity may be publication bias, small sample size, and low-quality literature. The random-effects model was used for statistical analysis. The combined SMD value in this study was 2.18, 95% CI (0.81, 3.55), and the difference was statistically significant, suggesting that probiotics are effective in enhancing FEV1/FVC function in children with asthma. In the SUCRA plot of the four interventions affecting FEV1/FVC (Figure 5), conventional treatment combined with probiotics has the largest area, indicating the highest probability of being the optimal intervention in improving the FEV1/FVC index in children with asthma.
Eleven forest plots labeled A to K display study data with horizontal lines representing 95% confidence intervals and diamonds indicating the overall effect sizes. Each plot shows individual studies on the vertical axis, with effect size and weight on the horizontal axis. Plots differ in studies included and effect sizes shown.
SUCRA plots for lung function, clinical effectiveness, recurrence rate, cytokines, and C-ACT scores. (A) FEV1/FVC; (B) FEV1%; (C) FEV1; (D) PEF; (E) total clinical effective rate; (F) recurrence rate; (G) IL-4; (H) IL-33; (I) C-ACT score. (1) Conventional treatment combined with Bifidobacterium triplex live bacteria; (2) conventional treatment combined with Bifidobacterium quadruplex live bacteria; (3) conventional treatment combined with Bifidobacterium–Lactobacillus triplex live bacteria; (4) conventional treatment combined with Bifidobacterium adolescentis; (5) conventional treatment combined with Saccharomyces boulardii; (6) conventional treatment combined with probiotics; (7) conventional treatment combined with Bacillus subtilis diplex live bacteria; (8) conventional treatment combined with Lactobacillus tablets; (9) conventional treatment combined with LF (LactobacillusBCRC 910259); (10) conventional treatment combined with LP (LactobacillusGMNL-133); (11) conventional treatment combined with LP+LF; (12) conventional treatment; (13) conventional treatment combined with placebo.
Nine line graphs labeled A to I show cumulative probability against rank with different colored dashed lines representing values 1, 2, 3, 4, 6, 7, 8, and 12. Each graph shows unique distribution patterns for these ranks.FEV<sub>1</sub>%
Six studies (11, 15–20) were included in the analysis for the pooled estimate analysis of FEV1%, and the network diagram (Figure 3) involved six interventions involving a total of 527 participants. Funnel plots were plotted, and Egger's test was performed to assess publication bias in the indicator. The funnel plots were significantly asymmetrical, suggesting a potential for publication bias. Egger's test was performed with a P-value of 0.058, which was not statistically significant, considering the marginal risk of publication bias in combination with the two. The heterogeneity test for the forest plot (Figure 4) showed I2 = 59.8%, P = 0.029, suggesting significant heterogeneity among the studies. Meta-regression and subgroup analysis of the included studies showed no decline in heterogeneity, suggesting that the sources of heterogeneity might be publication bias, small sample size, and low-quality literature. The random-effects model was used for statistical analysis, with a combined SMD value of 0.47, 95% CI (0.20, 0.75), and the difference was statistically significant, suggesting that probiotics are effective in improving FEV1% function in children with asthma. In the SUCRA ranking chart of the six interventions affecting FEV1% (Figure 5), conventional treatment combined with Bifidobacterium Lactobacillus triplex live bacteria has the largest area, indicating the highest probability of being the optimal intervention in improving the FEV1% index in children with asthma.
FEV<sub>1</sub>
Ten studies (10, 12–14, 17, 19, 21–24) reported FEV1, and the network diagram (Figure 3) involved eight interventions involving a total of 1,010 subjects. To assess publication bias for the indicator, funnel plots were plotted, and Egger's test was conducted. The funnel plots were symmetrically distributed, and Egger's test was performed with a P-value of 0.757, which was not statistically significant. Considering the lack of significant publication bias and the small sample effect, the results showed a lower risk of selective publication. The heterogeneity test for the forest plot (Figure 4) showed I2 = 88.7%, P = 0.000, suggesting significant heterogeneity among the studies. Meta-regression and subgroup analysis were conducted on the included studies, and the conclusion was that the number of strains (i.e., single strain and compound strain) was one of the sources of heterogeneity. After excluding the single strain, the heterogeneity test yielded I2 = 0.00%, P = 0.610. Two studies (14, 22) were identified as sources of heterogeneity. After excluding these two studies, the heterogeneity test yielded I2 = 0.00%, P = 0.510. Therefore, it is presumed that the sources of heterogeneity are these two studies and the number of strains. Using the random-effects model for statistical analysis, the combined SMD value was 0.99, 95% CI (0.59, 1.39), and the difference was statistically significant, suggesting that probiotics are effective in enhancing FEV1 function in children with asthma. In the SUCRA ranking chart of the eight interventions affecting FEV1 (Figure 5), conventional treatment combined with Bifidobacterium adolescentis has the largest area, indicating the highest probability of being the optimal intervention in improving FEV1 indicators in children with asthma.
PEF
Ten studies (10, 11, 14, 17–22, 24) reported PEF, and the network diagram (Figure 3) involved seven intervention measures, involving a total of 1,076 subjects. To assess publication bias for the indicator, funnel plots were plotted, and Egger's test was conducted. The funnel plots were relatively symmetrical in distribution, and Egger's test was performed with a P-value of 0.157, which was not statistically significant. No small sample effect was found. Considering the low risk of interference from publication bias, the results were relatively robust, and the reliability of the conclusion was guaranteed to some extent. The heterogeneity test for the forest plot (Figure 4) showed I2 = 96.8%, P = 0.000, suggesting significant heterogeneity among the studies. Meta-regression and subgroup analysis were conducted on the included studies, suggesting that the sources of heterogeneity might be publication bias, small sample sizes, and low-quality literature. Therefore, a random-effects model was used for statistical analysis. The combined SMD value was 1.76, 95% CI (0.74, 2.78), and the difference was statistically significant, suggesting that probiotics are effective in improving PEF function in children with asthma. In the SUCRA ranking chart of the seven interventions affecting PEF (Figure 4), conventional treatment combined with Lactobacillus tablets has the largest area, indicating the highest probability of being the optimal intervention in improving PEF indicators in children with asthma.
Secondary outcome measuresClinical total effective rate
Twenty-four studies (5, 10–12, 14, 15, 19, 21–23, 25–38) reported the clinical total effective rate, and the network diagram (Figure 3) involved 11 interventions involving a total of 2,768 participants. To assess the publication bias of this indicator, funnel plots were plotted, and Egger's test was conducted. The funnel plots were asymmetrically distributed left and right, with a small portion close to the bottom of the funnel, suggesting a possible small sample effect and publication bias. Egger's test was performed with a P-value of 0.012, and the slope was statistically significant, considering the small sample effect and publication bias in combination with the two. The heterogeneity test for the forest plot (Figure 4) showed I2 = 0%, P = 1.000, suggesting that the heterogeneity among the studies was small. A fixed-effect model was used for statistical analysis. The combined RR value was 1.18, 95% CI (1.14, 1.21), and the difference was statistically significant, suggesting that probiotics were effective in the clinical total effective rate of treating childhood asthma. In the SUCRA ranking chart of 13 interventions affecting total clinical efficacy (Figure 5), conventional treatment combined with Bacillus subtilis diplex live bacteria has the largest area in the SUCRA chart, indicating the highest probability of being the optimal intervention in improving the total clinical efficacy index in children with asthma.
Recurrence rate
Six studies (18, 25, 30, 35, 37, 38) reported recurrence rates, and the network diagram (Figure 3) involved six interventions involving a total of 544 subjects. To assess the publication bias of the indicator, funnel plots were plotted, and Egger's test was conducted. The funnel plots were asymmetrically distributed, mostly near the bottom of the funnel, suggesting a possible small sample effect. Egger's test was performed with a P-value of 0.069, which was not statistically significant, considering the marginal risk of the small sample effect in combination with the two. Subgroup analysis by follow-up duration (0–6 months vs. 6–12 months) showed that follow-up time was not a source of heterogeneity. The heterogeneity test for the forest plot (Figure 4) showed I2 = 0.00%, P = 0.528, suggesting that the heterogeneity among the studies was small. A fixed-effect model was used for statistical analysis. The combined RR value was 0.31, 95% CI (0.21, 0.48), and the difference was statistically significant, suggesting that probiotics are effective in reducing the recurrence rate of asthma in children. In the SUCRA ranking chart of the six interventions affecting the clinical total effective rate (Figure 5), conventional treatment combined with Bifidobacterium–Lactobacillus–Enterococcus triple probiotic has the smallest area, indicating the highest probability of being the optimal intervention in reducing the recurrence rate of asthma in children.
Adverse reactions
Six studies (3, 5, 12, 20, 26, 27) reported adverse reaction rates, with no statistically significant difference. The combination of probiotics did not increase the risk of adverse events compared to conventional treatment alone. Adverse reactions are shown in Table 2 (P > 0.05).
Adverse reactions.
Included studies
Experimental group
Control group
Adverse reactions
Total number
Adverse reactions
Total number
Sheng Ren 2020
3
42
5
42
Qibo Ma 2023
3
53
6
52
Jingfang Ma 2022
5
80
11
80
Rongrong Tuo 2023
7
43
6
43
Dan Wang 2023
16
250
19
250
Xiaofeng Yuan 2018
4
46
5
46
Total
38
514
52
513
Immune factors IgA and IgE
Three studies (27, 38, 39) reported IgA, and the network diagram (Figure 3) involved four interventions involving a total of 362 subjects. The heterogeneity test for the forest plot (Figure 4) showed I2 = 97.9%, P = 0.000, suggesting significant heterogeneity among the studies. Due to the small sample size, meta-regression and subgroup analysis were not conducted, and the random-effects model was used for statistical analysis. The combined SMD value was 2.11, 95% CI (0.29, 3.92), and the difference was statistically significant. That is, conventional treatment combined with probiotics was better than conventional treatment in improving IgA.
Four studies (9, 17, 32, 34) reported IgE, and the network diagram (Figure 3) involved four interventions involving a total of 372 subjects. The heterogeneity test for the forest plot (Figure 4) showed I2 = 95.2%, P < 0.001, suggesting significant heterogeneity among the studies. Due to the small sample size, meta-regression and subgroup analysis were not conducted, and the random-effects model was used for statistical analysis. The combined SMD value was −1.53, 95% CI (−2.69, −0.37), and the difference was statistically significant. Conventional treatment combined with probiotics was better than conventional treatment in improving IgE.
Cytokines IL-4, IL-33
Seven studies (12, 13, 16, 18, 19, 24, 33) reported IL-4. The network diagram (Figure 3) involved five intervention measures and a total of 665 subjects. Egger's test was performed on the data, with a P-value of 0.008, which was statistically significant, considering publication bias and small sample effects. The heterogeneity test for the forest plot (Figure 4) showed I2 = 95.4%, P = 0.000, suggesting significant heterogeneity among the studies. Meta-regression and subgroup analysis were conducted on the included studies, and the conclusion was that the Jadad score was one of the sources of heterogeneity. It was speculated that the sources of heterogeneity might be publication bias, small sample sizes, and low-quality literature. Random-effects models were used for statistical analysis. The combined SMD value was −1.87, 95% CI (−2.84, −0.9), and the difference was statistically significant, suggesting that conventional treatment combined with probiotics was better than conventional treatment in reducing IL-4. In the SUCRA ranking chart of the effects of IL-4 among the five interventions (Figure 5), conventional treatment combined with Bifidobacterium quadruplex live bacteria has the smallest area, indicating the highest probability of being the optimal intervention in improving IL-4 indicators in children with asthma.
Four studies (5, 18, 20, 24) reported IL-33, and the network diagram (Figure 3) involved five interventions involving a total of 386 subjects. Egger's test was performed with a P-value of 0.016, which was statistically significant, considering publication bias and small sample effect. The heterogeneity test for the forest plot (Figure 4) showed I2 = 95.4%, P = 0.000, suggesting significant heterogeneity among studies. Due to the small sample size, meta-regression and subgroup analysis were not conducted, and the random-effects model was used for statistical analysis. The combined SMD value was −1.94, 95% CI (−2.95, −0.93), and the difference was statistically significant. That is, conventional treatment combined with probiotics was better than conventional treatment in reducing IL-33. In the SUCRA ranking chart of the five interventions affecting IL-33 (Figure 5), conventional treatment combined with Bifidobacterium quadruplex live bacteria has the smallest area, indicating the highest probability of being the optimal intervention in improving IL-33 indicators in children with asthma.
C-ACT score
Four studies (10, 17, 29, 40) reported C-ACT scores, with network diagrams (Figure 3) involving four interventions involving a total of 318 subjects. The heterogeneity test for the forest plot (Figure 4) showed I2 = 73.3%, P = 0.011, suggesting significant heterogeneity among the studies. Due to the small sample size, meta-regression and subgroup analysis were not conducted, and the random-effects model was used for statistical analysis. The combined SMD value was 1.16, 95% CI (0.69, 1.63), and the difference was statistically significant. That is, conventional treatment combined with probiotics significantly improved C-ACT scores compared to conventional treatment alone. In the SUCRA ranking chart of the four interventions affecting the C-ACT score (Figure 5), conventional treatment combined with Bifidobacterium triplex live bacteria has the largest area, indicating the highest probability of being the optimal intervention in improving the C-ACT index in children with asthma.
Discussion
The Global Burden of Disease Study 2016 estimates that more than 339 million people worldwide have asthma, and the age-standardized prevalence has increased by 3.6% since 2006. First asthma often appears in early childhood, with global prevalence rates of childhood wheezing and adolescent wheezing being 11.7% and 14.1% respectively, increasing by an average of 0.13% and 0.06% (41) annually. Professor David Strachan believes that too little exposure of children to environmental bacteria can lead to an increased (41) risk of allergies and asthma. Based on this hypothesis, probiotics have been proposed for the prevention and treatment (42) of allergic diseases such as asthma. This network meta-analysis focused on clinical outcomes rather than mechanistic pathways, as the included RCTs primarily reported clinical endpoints. While our analysis cannot establish strain-specific mechanisms from clinical outcome data alone, published mechanistic studies suggest different mechanisms for various strains. For example, Bifidobacterium can stimulate the Th1/Th2 balance and upregulate the secretion of IFN-γ, IL-4, and IL-12 in the spleen. Lactobacillus plantarum can reduce the number of innate immune cells in the lungs and the levels of IL-6 and TNF-α in bronchoalveolar lavage fluid and induce immunosuppressive Treg responses (43) in the lungs.
In clinical practice, it is necessary not only to follow the principles of relevant guidelines and diagnosis and treatment norms to improve the accuracy (44) of diagnosis of childhood asthma but also to seek safer and more effective treatment regimens. Although probiotics are often used to treat childhood asthma, there are currently no standard and effective probiotic usage regimens, and there is a lack of comparative studies on the efficacy of probiotic treatment regimens. How to select more effective probiotic usage regimens for different clinical conditions is a concern in clinical practice.
This study included only one English study that met the criteria [the single English study (9) showed consistent effect directions with Chinese studies, though formal statistical comparison was not possible], and the quality assessment showed an overall low risk of bias and a moderate risk of bias. The analysis suggests that probiotics combined with conventional treatment can significantly improve lung function in children with asthma, increase the clinical total effective rate, reduce the recurrence rate, and may be effective by increasing IgA (45), reducing IgE, IL-4 (46), and IL-33 levels. Although some studies (47) have reported that probiotics have no statistical significance in improving FEV1, PEF, and C-ACT scores, which may be related to the small number of studies included, the non-exclusion of comorbidities and young children, this study believes that the combination of probiotics is effective. At the same time, given the favorable safety profile demonstrated (no increase in adverse events) and efficacy shown, probiotics may be considered as adjunctive therapy in childhood asthma management.
This meta-analysis found issues of publication bias and small sample effect in indicators such as pulmonary function and total clinical effective rate (funnel plot and Egger's test). Publication bias may arise from the difficulty in publishing negative results and the preference for positive results, which undermines data symmetry. Small sample studies tend to overestimate the true effect, and the combination of these increases the risk of “false positives.” The field should vigorously promote large sample sizes, encourage the public release of negative results, and reduce bias throughout the entire process from research design to dissemination. Despite the bias limitations of this analysis, the core conclusions still provide a reference for clinical and scientific research, and more high-quality studies are needed in the future to verify them.
For highly heterogeneous outcome measures, we conducted subgroup analyses and meta-regression on intervention measures (i.e., strain types), strain quantities (single strains or complex strains), treatment courses, manufacturers, and Jadad scores, but did not analyze the dosage of probiotics. The reasons are as follows: (1) weak evidence base (dose range 1–650 × 106 colony-forming units without established thresholds), making forced categorization prone to bias; (2) unclear dose–response relationships; (3) strain type and other factors having stronger confounding effects than dosage; and (4) studies focusing on strain–disease associations rather than dose optimization. The analysis did not identify the source of heterogeneity. It is presumed to be related to small sample size, publication bias, and low-quality literature. Therefore, given the high heterogeneity context, the pooled effect sizes provided should be considered exploratory estimates pending validation through high-quality RCTs. Due to the high risk of bias and heterogeneity, the quality of evidence suggesting probiotics improve FEV1/FVC, FEV1%, PEF, cytokines, and immune factors is low. The quality of evidence that probiotics improve FEV1, clinical total effective rate, and recurrence rate is moderate.
The validity of our indirect comparisons depends critically on the transitivity assumption. While we maintained consistency in age range (5–18 years) and disease severity (mild-to-moderate persistent asthma), variations in background conventional therapy may modify probiotic effects, representing a key limitation. (1) We strictly limited the subjects included in the study to children with asthma aged 5–18 years, ensuring comparability between studies in this dimension and meeting the transitivity requirement. (2) Although there was no uniform grading, we carefully examined the baseline characteristics of all included studies, which included children with mild-to-moderate persistent asthma. Therefore, there was good consistency in the characteristics of the study population in terms of disease severity. (3) There were differences in specific drug regimens among the studies, but these differences reflected the conventional range of practice for stepwise treatment of childhood asthma in the real world, and the core treatment principles were consistent. Therefore, we believe that good consistency was maintained among the studies on key patient characteristics, supporting the transmissibility hypothesis. The findings of this study emphasize the effect of probiotics as an add-on therapy to conventional treatment, and the background differences of conventional treatment should be taken into account when interpreting, with conclusions made cautiously.
Based on the SUCRA ranking analysis, conventional treatment combined with Bifidobacterium–Lactobacillus triplex live bacteria has the highest probability of being the optimal intervention in terms of increasing FEV1% and reducing recurrence rate. Studies (48, 49) suggest that it may promote asthma recovery by suppressing inflammatory markers such as serum chemokine-like factor-1 and nerve growth factor, and by enhancing dendritic cell (DC) and T-cell activity to regulate immune function. Conventional treatment combined with Bifidobacterium adolescentis has the highest probability of being the optimal intervention in increasing FEV1. This may be related to its upregulation of CD86 expression to promote DC maturation and stimulate DC secretion of IL-12 and IFN-γ, thereby altering Th2 dominant differentiation and correcting Th1/Th2 imbalance (50). Conventional treatment combined with Lactobacillus tablets has the highest probability of being the optimal intervention in increasing PEF. Animal studies (51) support lactic acid bacteria in improving asthma responses by inducing IL-10 production, downregulating Th1/Th2 responses, and inhibiting eosinophilic inflammation. Conventional treatment combined with Bacillus subtilis diplex live bacteria has the highest probability of being the optimal intervention in terms of improving the clinical total effective rate. Conventional treatment combined with Bifidobacterium quadruplex live bacteria has the highest probability of being the optimal intervention in reducing IL-4 and IL-33 levels. Conventional treatment combined with Bifidobacterium triplex live bacteria has the highest probability of being the optimal intervention in improving the C-ACT score. It is notable that, with the exception of a few outcome measures such as IgA and FEV1, most SUCRA ranking differences were not statistically significant (95% CI overlap rate >75%). Therefore, when making clinical decisions, the uncertainty reflected by the probability ranking results and the range of confidence intervals should be taken into account.
Comparison with other network meta-analyses
Over the past decade or so, studies on probiotics for childhood asthma have mainly focused on preventive effects and the exploration (52–54) of mechanisms of action, and there is no meta-analysis similar to this one. Western studies have focused more on early life intervention to prevent asthma development (developmental origins hypothesis), while the included studies mainly addressed clinical management of children with diagnosed asthma (primarily Chinese children). Due to the scarcity of eligible Western studies, the results cannot be directly extrapolated to Western populations at present.
Strengths and limitations of this studyStrengths
This study innovatively compares the regimens and efficacy of different types of probiotics in the treatment of childhood asthma, providing evidence-based medical support for standardizing the application of probiotics in the treatment of this disease. Excluding children under 5 years ensured diagnostic accuracy and internal validity, though this limits applicability to early childhood when probiotic interventions may have greater immunomodulatory potential. To ensure homogeneity and internal effectiveness of the included studies, we strictly limited the age range to 5–18 years. Asthma phenotypes, diagnostic criteria, and outcome measurement methods were relatively more consistent in children within this age group. Including groups with a wide age range, such as infants and adolescents, would introduce uncontrollable sources of heterogeneity, making the interpretation of the combined effect size ambiguous or even misleading. The 5–18 age group represents an important and large subgroup in asthma management, and its treatment effect assessment is equally instructive for clinical practice. Our study fills the evidence gap for this specific age group. The reliability of the study method was demonstrated through meta-regression and subgroup analyses of interventions, strain counts, etc.
Limitations
Although this study included studies with a sample size of more than 30 cases, which was somewhat credible, there were still limitations. (1) The predominance of Chinese studies (33/34) limits generalizability to Western populations. Our comprehensive search strategy included major English databases (PubMed, Web of Science); the low yield of English studies reflects the current evidence landscape rather than selective searching. (2) Future systematic reviews should prioritize inclusion of international studies to enable robust cross-cultural comparisons. (3) Differences in dosing time, dose–course, follow-up time, and evaluation indicators among different studies led to high heterogeneity in the meta-analysis. The total sample size of the included studies was small. (4) Excluding children <5 years ensures diagnostic accuracy but limits applicability to early childhood, when probiotic interventions may have greater immunomodulatory potential. There is an urgent need for well-designed RCTs in the future to use age-appropriate validation indicators to evaluate the efficacy of probiotics for asthma (or recurrent wheezing) in infants and young children.
In addition, 12 studies used multi-strain probiotic formulations analyzed as combined interventions, as individual strain contributions could not be separated. Most included studies used compound probiotic preparations where synergistic or antagonistic effects between strains remain unexplored, making it difficult to attribute efficacy to specific strains. Future studies need to strengthen strain-specific design and delve deeper into the mechanisms and clinical effects of individual strains to provide more precise guidance for application. Four interventions were supported by single studies only, limiting confidence in their rankings. Interventions supported by ≤2 studies require confirmatory trials before clinical recommendations.
The observed differences in SUCRA rankings may reflect the combined effects of strain properties, dosing, duration, and patient factors rather than strain-specific mechanisms alone. Direct mechanistic comparisons require specifically designed trials with biomarker assessments.
Some of the randomized controlled studies published domestically failed to describe in detail the random allocation method, allocation concealment, and blinding or had problems with the research methods themselves, which to some extent affected the methodological quality of the studies and posed a risk of bias in the results. Therefore, higher-quality, standardized design, large-sample, RCTs are still needed to evaluate the actual efficacy of probiotics to provide better evidence for probiotics in the treatment of childhood asthma.
Prospects for the future
Traditional Chinese medicine has unique advantages in treating childhood asthma (such as personalized herbal medicine, simple and well-tolerated external treatment methods), and related research is increasing. However, no RCTs of probiotics combined with traditional Chinese medicine or external treatment of traditional Chinese medicine for childhood asthma were found in this search, and the combined effect remains unknown and awaits exploration. This study confirmed the effectiveness of probiotics as an adjunctive treatment for childhood asthma and provided a direction for strain selection. Future research should focus on the following areas. (1) Fill the evidence gap: There is an urgent need to conduct high-quality RCTs to evaluate the efficacy of probiotics in infantile asthma/recurrent wheezing using age-appropriate indicators. (2) In-depth mechanism and precision: Dose-standardization studies should be conducted to clarify strain-specific dose-effect relationships. (3) Exploring combination options: further research should investigate probiotics and traditional Chinese medicine (internal/external) combination therapy to provide a more comprehensive and effective treatment strategy for childhood asthma by advancing research to a deeper and more precise level.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Natural Science Foundation of Jiangxi, China (grant number 20224BAB216095) and the National Natural Science Foundation of China (grant number 82305314).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Correction Note
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