Immune risk factors for recurrent respiratory infections after Mycoplasma pneumoniae infection in children: a retrospective study

Abstract

Background

Mycoplasma pneumoniae (M. pneumoniae) is a common cause of respiratory tract infections in children, but its association with subsequent recurrent respiratory tract infections (RRTIs) remains unclear. This study aimed to identify immunological factors associated with RRTIs following M. pneumoniae infection.

Methods

We retrospectively reviewed hospital records of children aged 0–14 years who were consecutively hospitalized with laboratory confirmed M. pneumoniae infection between March 2023 and March 2024. Demographic and baseline clinical characteristics, laboratory results, and imaging findings during hospitalization were collected. Cases were classified as mild or severe according to predefined criteria. RRTIs occurring within 12 months after the index hospitalization were identified through review of subsequent hospital visits and documented diagnoses; patients with incomplete records or missing follow-up data were excluded from recurrence analyses.

Results

Among 265 hospitalized children, 196 (73.9%) had mild disease and 69 (26.1%) had severe disease. Severe infection was associated with lower CD3⁺ T-cell counts (P=0.001) and higher IL-1β levels (P=0.03). During follow-up, children who developed RRTIs exhibited higher CD3⁺ [odds ratio (OR) =1.10, 95% confidence interval (CI): 1.02–1.18, P=0.009], CD4⁺ (OR = 1.08, 95% CI: 1.01–1.15, P=0.045), and higher IgA levels (OR =1.35, 95% CI: 1.01–1.80, P=0.04), indicating these immunological factors were independently associated with recurrent infections.

Conclusions

Distinct immunological alterations during acute M. pneumoniae infection were associated with disease severity and subsequent RRTIs in hospitalized children. Assessment of cellular and humoral immune parameters may help identify children at increased risk of recurrent infections.

Highlight box.

Key findings

• Stratified analysis by disease severity shows that severe cases have increased pro-inflammatory cytokines, correlating with clinical severity scores.

• Children with recurrent respiratory tract infections (RRTIs) exhibit distinct cellular immune profiles, including higher CD3⁺ and CD4⁺ T cell proportions, and altered cytokine levels (IL-1β, IFN-γ, CCL-5, CXCL-10) compared with non-recurrent cases.

What is known and what is new?

• It is known that recurrent infections in children are associated with immune dysregulation, but detailed profiles of humoral and cellular immunity in RRTIs remain unclear.

• This study provides novel evidence of specific immune alterations and cytokine patterns in pediatric RRTIs, highlighting potential biomarkers for disease severity and risk stratification.

What is the implication, and what should change now?

• These findings suggest that monitoring immune cell subsets and cytokine levels could guide early identification and management of children at risk for recurrent respiratory infections.

• Interventions targeting immune modulation may help reduce infection recurrence and improve clinical outcomes in this population.

Introduction

Mycoplasma pneumoniae (M. pneumoniae) is a common etiological agent of respiratory tract infections in children, with its global incidence increasing in recent years. Clinical manifestations range from asymptomatic colonization to a broad spectrum of upper and lower respiratory tract infections, including nasopharyngitis, sinusitis, otitis media, tonsillopharyngitis, tracheobronchitis, bronchiolitis, and pneumonia. Beyond respiratory symptoms, M. pneumoniae is associated with a wide range of extrapulmonary manifestations—such as dermatologic, neurologic, hematologic, cardiac, musculoskeletal, and gastrointestinal involvement, which are increasingly recognized, particularly in children. These extrapulmonary effects are believed to result from a combination of direct pathogen invasion, immune complex deposition, and autoimmune responses triggered by molecular mimicry, as demonstrated in recent studies (1).

Mycoplasma pneumoniae pneumonia (MPP) accounts for approximately 8–40% of community-acquired pneumonia (CAP) cases in children aged 3–15 years, with regional epidemics occurring every 3–7 years (2). In China, M. pneumoniae is a leading cause of CAP among preschool- and school-aged children, as noted in the 2024 Chinese Guidelines for the Diagnosis and Treatment of Pediatric CAP (3). While infants aged 1–3 years may also be affected, the disease course in this age group is typically mild. However, the incidence of severe MPP has increased, frequently requiring hospitalization and leading to complications such as atelectasis, necrotizing pneumonia, and extrapulmonary involvement (4). Globally, M. pneumoniae contributes 10–40% of pediatric CAP cases, with high transmissibility in schools and daycare centers facilitating outbreaks among immunologically naive populations (5).

Although most M. pneumoniae infections are self-limiting, severe cases may result in significant pulmonary and extrapulmonary complications, adversely affecting clinical outcomes and quality of life. RRTIs affect 10–15% of children worldwide and up to 18% in China (6-8), causing chronic airway inflammation, reduced lung function, and increased susceptibility to respiratory disease.

Previous studies have reported that a considerable proportion of children develop recurrent respiratory tract infections within the first year following acute Mycoplasma pneumoniae infection, highlighting a clinically relevant but incompletely understood problem. The mechanisms underlying this post-infectious susceptibility remain poorly defined. In recent years, increasing attention has been directed toward the concept of “immune debt” or an “immunity gap” following periods of reduced pathogen exposure, which may impair host immune maturation and contribute to heightened vulnerability to recurrent infections in the post-pandemic era (9). Despite these observations, robust clinical or immunological markers capable of reliably identifying children at increased risk of recurrent respiratory tract infections after M. pneumoniae infection are still lacking.

The development of RRTIs is multifactorial, involving complex interactions among host immune dysregulation, genetic susceptibility, and environmental exposures. Although prior studies suggest a relationship between M. pneumoniae infection and subsequent recurrent respiratory illness (10,11), immunological mechanisms remain poorly defined. The airway epithelium, as the initial interface between pathogen and host, orchestrates innate immune responses through cytokine and chemokine release, regulating immune cell recruitment. Dysregulation of these epithelial-driven innate immune processes may promote a persistent inflammatory state within the airways, increasing susceptibility to recurrent infections.

Accumulating evidence suggests that immune imbalance following M. pneumoniae infection—rather than pathogen exposure alone—may be a critical determinant of long-term respiratory outcomes. Elevated IgE levels and dysregulated T helper cell responses have been reported in affected children (12-14). Inflammatory mediators such as CCL5, CXCL10, and IFN-γ are involved in immune cell trafficking and sustained airway inflammation (15-17), while activation of the NLRP3 inflammasome and subsequent IL-1β release may further amplify inflammatory injury and tissue damage (18). In addition, alterations in immune cell populations, including T lymphocytes, B cells, and natural killer (NK) cells, as well as impaired humoral immunity reflected by abnormal IgA and IgG levels, may indicate an underlying immune or inflammatory predisposition in certain children.

These observations indicate that individual immune characteristics, reflecting the interplay between airway innate responses and systemic immune regulation, may influence disease evolution after M. pneumoniae infection. Such underlying immune predisposition may contribute to the variability of clinical outcomes in children, including susceptibility to recurrent respiratory tract infections. Accordingly, this prospective study sought to identify immune-related risk factors for RRTIs following M. pneumoniae infection by assessing cytokine profiles, immunoglobulin levels, and immune cell subsets. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-825/rc).

Methods

Study design and participants

This retrospective observational study reviewed electronic medical records of hospitalized children aged 0–14 years with laboratory-confirmed M. pneumoniae infection at Chengdu Women and Children’s Central Hospital between March 2023 and March 2024. Eligible patients were identified using predefined inclusion and exclusion criteria, and consecutive cases within the study period were included to minimize selection bias. Clinical, laboratory, and imaging data were extracted using a standardized protocol. Key records and imaging findings were independently reviewed by two pediatric respiratory physicians, with discrepancies resolved by consensus, to reduce information bias.

Study size

Because this was a retrospective study, no a priori sample size calculation was performed. To evaluate whether the available sample provided sufficient statistical power for the main immunological comparisons, a post-hoc power analysis was conducted based on observed effect sizes for key cytokines and lymphocyte subsets. The analysis indicated that the sample size was adequate to detect moderate between-group differences; however, smaller effect sizes may have been underpowered.

Ethical statement

The study protocol was reviewed and approved by the Ethics Committee of Chengdu Women and Children’s Central Hospital (approval No. 2023(32)-2), and the study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All clinical data were anonymized prior to analysis to ensure patient confidentiality. Written informed consent to participate was obtained from the parents or legal guardians of all participants under the age of 16 years.

Inclusion and exclusion criteria

Diagnostic criteria

The diagnosis of M. pneumoniae infection was based on the following laboratory findings: (I) positive detection of M. pneumoniae RNA in a throat swab specimen using nucleic acid amplification testing (NAAT); (II) serological evidence of recent or acute infection, defined as a single serum M. pneumoniae-specific IgM titer ≥1:160, measured via immunogold colloid assay.

These diagnostic criteria align with both national and international guidelines for M. pneumoniae infection (1,19).

Disease severity was assessed according to the 2024 Chinese Guidelines for the Diagnosis and Treatment of CAP in Children (1,20). For international comparability, classification also incorporated criteria from the Infectious Diseases Society of America (IDSA) and the Pediatric Infectious Diseases Society (PIDS) guidelines on pediatric CAP (21). Patients meeting criteria for severe pneumonia—such as respiratory distress, hypoxia, multilobar infiltration, or hospitalization—were categorized as severe M. pneumoniae pneumonia (SMPP) (20); all others were classified as mild.

Severe pneumonia criteria

Patients were classified as having severe pneumonia if they met one or more of the following conditions (19): (I) blood oxygen saturation ≤92%; (II) poor general condition (e.g., refusal to eat, dehydration, altered consciousness); (III) markedly increased respiratory rate with signs of respiratory distress (e.g., cyanosis, dyspnea, nasal flaring, chest retractions); (IV) involvement of multiple lung lobes or ≥2/3 of lung fields, with or without pleural effusion or extrapulmonary complications.

Inclusion criteria

(I) Age between 1 month and 14 years; (II) presence of clinical symptoms of respiratory tract infection, such as fever, cough, wheezing, or shortness of breath; (III) availability of complete 12-month clinical follow-up data.

The age range of 1 month to 14 years was selected to focus on infants, preschool-aged, and school-aged children, who represent the most vulnerable populations for both acute and recurrent M. pneumoniae-related respiratory infections. Although the international pediatric definition extends to 17 years of age, adolescents aged 15–17 were excluded to reduce heterogeneity in immune development, clinical presentation, and treatment patterns. This restriction improved internal consistency and interpretability of the findings.

Exclusion criteria

(I) Presence of congenital immunodeficiency, autoimmune or rheumatic immune diseases, or long-term immunosuppressive therapy; (II) missing key clinical records or incomplete outpatient follow-up within 1 year.

Microbiological testing

All patients underwent comprehensive pathogen screening within 24 hours of admission.

1. M. pneumoniae DNA detection from nasopharyngeal swabs was performed using real-time PCR with the M. pneumoniae PCR Detection Kit (Tian Long Tech, Xi’an, China), which has a lower detection limit of 100 copies/mL. PCR assays were conducted on the ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) following the manufacturer’s protocol.

2. Serological testing for IgM antibodies against M. pneumoniae and common respiratory viruses was conducted using commercial ELISA kits (Mike Bio, Co., Ltd., Shanghai, China), in accordance with the manufacturer’s instructions. Optical density (OD) was measured at 450 nm using a microplate reader (Bio-Rad, Hercules, CA, USA).

3. Passive particle agglutination testing for M. pneumoniae-specific antibodies was performed using the SERODIA MYCO-II diagnostic kit (Zhuhai Lizhu Reagent Co., Ltd., Zhuhai, China), which detects both IgM and IgG. Serum samples were serially diluted with the supplied buffer. Equal volumes of diluted serum and sensitized gelatin particles were added to 96-well microplates, gently mixed, and incubated at 20–25 °C for 2 hours. Agglutination was visually assessed. A titer ≥1:160 was considered indicative of recent or active infection.

Laboratory and imaging data collection

Clinical and diagnostic data were extracted from electronic medical records and categorized as follows:

Demographic and clinical characteristics

Data included sex, age, clinical symptoms and signs, season of onset, relevant medical history (personal and familial), and diagnostic findings (including etiological and imaging results).

Inflammatory and biochemical markers

Serum levels of interleukin-1β (IL-1β), chemokine ligand 5 (CCL5), CXC chemokine ligand 10 (CXCL10), and interferon-gamma (IFN-γ) were quantified using a double-antibody sandwich ABC-ELISA method. Commercial kits were obtained from Shanghai Xitang Biotechnology Co., Ltd. (Shanghai, China), with catalog numbers: IL-1β (XT-ELISA-F01220), CCL5 (XT-ELISA-F02410), CXCL10 (XT-ELISA-F01551), and IFN-γ (XT-ELISA-F01090).

The assay protocol was as follows.

1. 100 µL of standards or serum samples was added to each well and incubated at 37 °C for 40 minutes.

2. Wells were washed 4–6 times with wash buffer, and excess liquid was removed using absorbent paper.

3. 50 µL of distilled water and 50 µL of primary antibody solution were added (blank wells received 100 µL of distilled water), followed by incubation at 37 °C for 20 minutes.

4. Plates were washed again as above.

5. 100 µL of enzyme-labeled antibody solution was added and incubated at 37 °C for 10 minutes.

6. After another wash cycle, 100 µL of substrate solution was added and incubated in the dark at 37 °C for 15 minutes.

7. The reaction was terminated with 100 µL of stop solution.

8. OD was measured at 450 nm within 30 minutes using a DENLEY DRAGON Wellscan MK 3 microplate reader (Thermo, Vantaa, Finland). Data analysis was conducted using Ascent Software for Multiskan (Thermo).

For result calculation, blank OD values were subtracted prior to analysis (this step was omitted if the blank OD was <0.1). A standard curve was generated by plotting known concentrations (2,000, 1,000, 500, 250, 125, 62.5, 31.2, and 0 pg/mL) on the X-axis against corresponding OD values on the Y-axis. Curve fitting was conducted using a four-parameter logistic (4-PL) regression model. Sample concentrations were interpolated from the standard curve and adjusted by the dilution factor.

Immunological assessment

Serum immunoglobulin (IgG, IgA, IgM) and complement (C3, C4) concentrations were measured by immunoturbidimetric assay using a HITACHI 008AS automatic biochemical analyzer (Hitachi Ltd., Tokyo, Japan) and matching reagent kits from Mike Biopharmaceuticals (Chengdu, China; IgG: MIK-IGG-001, IgA: MIK-IGA-001, IgM: MIK-IGM-001, C3: MIK-C3-002, C4: MIK-C4-002). (I) Serum was mixed with specific antisera to form immune complexes, generating turbidity proportional to the analyte concentration. The analyzer was calibrated with a multi-point standard curve provided in the reagent kit. Turbidity was measured at 340 nm. Each sample was tested in duplicate, and the average value was recorded. (II) Concentrations were automatically calculated by system software using linear or spline interpolation based on standard absorbance values. Quality control samples at high, medium, and low concentrations were included in each assay run.

Lymphocyte subsets including CD3⁺ T cells, CD4⁺ helper T cells, CD8⁺ cytotoxic T cells, CD19⁺ B cells, and CD16⁺/CD56⁺ NK cells—were analyzed by flow cytometry using a BD FACSCanto II (BD Biosciences, San Jose, CA, USA). Whole blood was collected in EDTA-K₂ tubes and analyzed within 24 hours. (I) Fluorescently labeled monoclonal antibodies (anti-CD3-FITC, anti-CD4-PC7, anti-CD8-APC-Cy7, anti-CD19-APC, anti-CD56-PE) were obtained from Beijing Tongsheng Times Biotechnology Co., Ltd. (Beijing, China) and premixed per the manufacturer's instructions. (II) Briefly, 20 µL of antibody mixture was added to each tube, followed by 50 µL of whole blood. Tubes were gently vortexed and incubated in the dark at room temperature for 15–25 minutes. Then, 450 µL of 1× red blood cell lysing solution (1:9 dilution of 10× stock) was added. Samples were mixed and incubated in the dark for an additional 15 minutes. (III) Acquisition and analysis were conducted using BD FACSDiva software version 8.0. A minimum of 10,000 lymphocyte events was collected per sample, and gating strategies were applied to determine percentages and absolute counts of lymphocyte subsets. (IV) Quality control was ensured using instrument-specific calibration beads and commercial human lymphocyte control materials (e.g., CD-Chex^® Plus, Streck Inc., Omaha, NE, USA; FDA registration number K960894). Daily quality control followed manufacturer protocols.

Due to sample limitations, double-negative T cells (DNTs) (CD3⁺CD4⁻CD8⁻) were not assessed but represent a topic of interest for future studies given their potential role in post-infectious immune dysregulation.

Serum immunoglobulin and complement concentrations, as well as lymphocyte subsets, were assessed at hospital admission during the acute phase of M. pneumoniae infection. Because of the retrospective design, all laboratory testing was performed as part of routine clinical care at presentation. It is acknowledged that acute infection may transiently affect immune parameters, which is considered a study limitation and addressed in the Discussion.

Diagnostic criteria for RRTIs

Respiratory Infections issued by the Respiratory Group of the Pediatrics Branch of the Chinese Medical Association (2008) (22), the diagnostic criteria for RRTIs are as follows: ages 0–2 years: ≥7 episodes of upper respiratory tract infection, ≥3 episodes of bronchitis, or ≥2 episodes of pneumonia per year; ages 3–5 years: ≥6 upper respiratory infections, ≥2 bronchitis episodes, or ≥2 pneumonia episodes per year; ages 6–14 years: ≥5 upper respiratory infections, ≥3 bronchitis episodes, or ≥3 pneumonia episodes per year.

During follow-up, participants were classified into two groups based on whether recurrent respiratory tract infections occurred: the RRTIs group and the control group.

After 1 year of follow-up, clinical data—including sex, age, M. pneumoniae titer, genotype, immunoglobulin levels, lymphocyte subsets, cytokines, and chemokines—were compared between groups to identify risk factors for secondary RRTIs following M. pneumoniae infection.

RRTIs within 12 months were identified through review of hospital records and outpatient visits. Patients with incomplete records or lost to follow-up were excluded from the recurrence analysis.

Data and statistical analysis

Statistical analyses were performed using SPSS version 25.0. Continuous variables with normal distribution are presented as mean ± standard deviation and compared using independent samples t-tests. Non-normally distributed data are expressed as median (P25–P75) and compared using non-parametric tests. Categorical variables are presented as percentages and analyzed using the chi-squared (χ²) test.

Variables with significant intergroup differences were further analyzed using multivariate stepwise logistic regression to identify independent predictors of RRTIs. Recurrence within 12 months was treated as a binary outcome, and reported odds ratios (ORs) are adjusted for age and sex. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values for each predictor. Two-sided P values <0.05 were considered statistically significant.

The amount of missing data was minimal (<5%). Therefore, a complete-case analysis was performed, and no imputation procedures were required. Important environmental and host factors including atopy, daycare attendance, passive smoking exposure, and breastfeeding history were not systematically available in the retrospective records and could not be adjusted for in the analyses.

Results

Clinical presentation

This study compared clinical characteristics between children with mild (n=196) and severe (n=69) M. pneumoniae infection. The mild group included 45.9% males and 54.1% females, with a mean age of 4.92±2.17 years, while the severe group comprised 53.6% males and 46.4% females, with a mean age of 4.86±2.85 years. No statistically significant differences in age or sex distribution were observed between groups. The incidence of infections peaked in July and August in the mild group, and in June and July in the severe group. Common clinical symptoms in both groups included cough, fever, and wheezing. However, children in the severe group were more likely to present with chest pain (thoracodynia), shortness of breath, and dyspnea, suggesting more pronounced respiratory distress. Regarding complications, the mild group experienced a broader range, including asthma (14.8%), cough-variant asthma (2.6%), and febrile convulsions (0.5%). In contrast, the predominant complication in the severe group was asthma (14.5%) (Table 1). These findings suggest that although epidemiological and genotypic characteristics were similar, children with severe infection exhibited more significant clinical signs of respiratory compromise.

Table 1. Demographic characteristics, clinical features, and follow-up data of children with Mycoplasma pneumoniae infection stratified by disease severity and recurrence status.

Characteristics	Mild			Severe
	RRTI (n=24)	NRRTI (n=172)		RRTI (n=8)	NRRTI (n=61)
Male	10 (41.7)	80 (46.5)		4 (50.0)	33 (54.1)
Age (years)	4.90±2.18	4.92±2.17		4.88±2.96	4.86±2.85
Onset season	July–August	July–August		June–July	June–July
Clinical signs and symptoms	Cough, fever, wheezing	Cough, fever, wheezing		Cough, fever, wheezing, thoracodynia, dyspnea	Cough, fever, wheezing, thoracodynia, dyspnea
Complications
Asthma	4 (16.7)	25 (14.5)		1 (12.5)	9 (14.8)
CVA	1 (4.2)	4 (2.3)		0	0
Febrile convulsion	0	1 (0.6)		0	0
URTI in 1-year follow-up	24 (100.0)	2 (1.2%)		8 (100.0)	0
LRTI in 1-year follow-up	24 (100.0)	86 (50.0)		8 (100.0)	31 (50.8)
Use of immunomodulators*	12 (50.0)	15 (8.7)		5 (62.5)	8 (13.1)

Data are presented as mean ± standard deviation or n (%). *, immunomodulators refer to oral or intravenous agents, including thymosin, pidotimod, or immunoglobulin. CVA, cough variant asthma; LRTI, lower respiratory tract infection; NRRTI, non-recurrent respiratory tract infection; RRTI, recurrent respiratory tract infection; URTI, upper respiratory tract infection.

Stratified analysis by disease severity

Subgroup analysis (Figure 1) comparing mild and severe cases of MPP revealed that CD3⁺ and CD4⁺ T cell percentages were significantly higher in the severe group (68.91% vs. 64.30%, P<0.001; and 37.34% vs. 33.83%, P=0.001, respectively) (Table 2). However, there were no significant intergroup differences in humoral immunity markers (IgG, IgA, IgM; Table 2) or inflammatory cytokine levels (Table 3). These findings suggest that increased T cell activation may be associated with both RRTIs and disease severity.

Figure 1.

Combined heatmap of immune and inflammatory correlations. CD3⁺ T cells showed a strong positive correlation with CD4⁺ T cells. IL-1β demonstrated partial correlation with IFN-γ and CXCL-10, suggesting shared inflammatory signaling pathways. Additionally, a moderate correlation was observed between B cell proportions and immunoglobulin levels. CCL5, C-C motif chemokine ligand 5; CXCL10, C-X-C motif chemokine ligand 10; IFN-γ, interferon gamma; Ig, immunoglobulin; IL-1β, interleukin-1 beta; NK, natural killer.

Table 2. Immune function test results.

Immunological indices	Mild	Severe	P value
Humoral immunity (g/L)
IgG	8.92±2.27	8.54±1.37	0.10
IgA	1.31±0.66	1.33±0.55	0.80
IgM	1.46±0.62	1.38±0.34	0.17
C3	1.27±0.25	1.24±0.15	0.17
C4	0.28±0.11	0.28±0.10	0.73
Cellular immunity (%)
CD3	64.30±10.11	68.91±6.41	<0.001*
CD4	33.83±8.12	37.34±6.73	0.001*
CD8	24.00±6.65	25.25±5.50	0.13
NK	12.75±6.86	12.74±5.48	0.99
B	20.60±9.37	17.67±5.72	0.003*

Data are presented as mean ± standard deviation. *, P<0.05 was considered statistically significant. Ig, immunoglobulin; NK, natural killer.

Table 3. Cytokine profile in children with Mycoplasma pneumoniae infection.

Cytokines	Mild	Severe	P value
IL-1β (pg/mL)	38.32 (22.02–61.81)	35.33 (19.91–56.10)	0.33
IFN-γ (pg/mL)	58.86 (30.13–126.29)	47.88 (24.95–78.64)	0.15
CCL-5 (pg/mL)	886.08±130.06	899.96±83.31	0.18
CXCL-10 (pg/mL)	90.63 (56.44–143.81)	72.65 (39.80–139.99)	0.17

Data are presented as mean ± standard deviation or median (P25–P75). No statistically significant differences. CCL5, C-C motif chemokine ligand 5; CXCL10, C-X-C motif chemokine ligand 10; IFN-γ, interferon gamma; IL-1β, interleukin-1 beta.

Clinical and immunological differences between RRTIs and NRRTIs

A total of 265 children with M. pneumoniae infection were included in the analysis, of whom 119 developed RRTIs. As illustrated in Figure 2 (error bar plot), children in the RRTI group exhibited significantly higher levels of CD3⁺ (66.86% vs. 63.81%, P=0.009), CD4⁺ (35.61% vs. 33.66%, P=0.045), and IgA (1.38 vs. 1.23 g/L, P=0.04) compared with those in the non-recurrent respiratory tract infection (NRRTI) group (Table 4). No statistically significant differences were observed between groups in IgG, IgM, NK cell percentage, or B cell percentage (P>0.05; Table 4). Among inflammatory cytokines, IL-1β was significantly lower in the RRTI group (median 34.91 vs. 42.89 pg/mL, P=0.03), whereas IFN-γ, CXCL-10, and CCL-5 levels showed no significant differences (Table 5).

Figure 2.

Error bar plot illustrating mean ± SD distributions of immune and inflammatory markers in children with mild versus severe M. pneumoniae infection. CD3⁺, CD4⁺, and B cell levels were significantly higher in the severe group (P<0.01). Despite overlapping standard deviation ranges for several indicators, statistically significant differences were observed for these three markers. CCL5, C-C motif chemokine ligand 5; CXCL10, C-X-C motif chemokine ligand 10; IFN-γ, interferon gamma; Ig, immunoglobulin; IL-1β, interleukin-1 beta; NK, natural killer; SD, standard deviation.

Table 4. Immune function test results in children with and without recurrent respiratory tract infections.

Immunological indices	RRTIs	NRRTIs	P value
Humoral immunity (g/L)
IgG	8.97±2.06	8.64±2.09	0.19
IgA	1.38±0.62	1.23±0.65	0.04*
IgM	1.45±0.53	1.43±0.59	0.74
C3	1.28±0.21	1.25±0.25	0.35
C4	0.28±0.10	0.28±0.11	0.68
Cellular immunity (%)
CD3	66.86±9.02	63.81±9.84	0.009*
CD4	35.61±8.36	33.66±7.22	0.045*
CD8	25.04±6.53	23.44±6.12	0.04*
NK	12.28±6.08	13.34±7.01	0.19
B	19.25±8.22	20.57±9.16	0.22

Data are presented as mean ± standard deviation. *, P<0.05 was considered statistically significant. Ig, immunoglobulin; NK, natural killer; NRRTIs, non-recurrent respiratory tract infections; RRTIs, recurrent respiratory tract infections.

Table 5. Cytokine profiles in children with and without recurrent respiratory tract infections.

Cytokines	RRTIs	NRRTIs	P value
IL-1β (pg/mL)	34.91 (20.63–55.27)	42.89 (22.70–66.88)	0.03*
IFN-γ (pg/mL)	53.43 (27.45–84.88)	58.01 (33.08–144.70)	0.13
CCL-5 (pg/mL)	898.56±114.32	878.64±125.62	0.18
CXCL-10 (pg/mL)	86.82 (51.54–139.39)	88.25 (57.15–153.66)	0.49

Data are presented as mean ± standard deviation or median (P25–P75). *, P<0.05 was considered statistically significant. CCL5, C-C motif chemokine ligand 5; CXCL10, C-X-C motif chemokine ligand 10; IFN-γ, interferon gamma; IL-1β, interleukin-1 beta; NRRTIs, non-recurrent respiratory tract infections; RRTIs, recurrent respiratory tract infections.

Standardized immune profile visualization

To visualize the distribution of immune and inflammatory indicators, a Z-score–normalized combined heatmap was generated (Figure 3). The RRTIs group exhibited a distinct clustering pattern, characterized by elevated CD3⁺, CD4⁺, and IgA levels, along with reduced IL-1β levels, compared with the NRRTIs group (Table 5). This pattern suggests that children with RRTIs tend to display enhanced adaptive immune responses and relatively diminished innate immune activation.

Figure 3.

Heatmap of Z-score-standardized immune and inflammatory profiles comparing children with and without RRTIs. Distinct clustering patterns were observed, with increased CD3⁺, CD4⁺, and IgA levels and decreased IL-1β levels in the RRTI group. CCL5, C-C motif chemokine ligand 5; CXCL10, C-X-C motif chemokine ligand 10; IFN-γ, interferon gamma; Ig, immunoglobulin; IL-1β, interleukin-1 beta; NK, natural killer; NRRTIs, non-recurrent respiratory tract infections; RRTIs, recurrent respiratory tract infections.

Logistic regression identifies predictors of RRTIs

Multivariate logistic regression was performed to identify independent predictors of RRTIs (Figure 4, forest plot). Elevated CD3⁺ T cell percentage (OR =1.10, 95% CI: 1.02–1.18, P=0.009), CD4⁺ T cells (OR =1.08, 95% CI: 1.01–1.15, P=0.045), and IgA levels (OR =1.35, 95% CI: 1.01–1.80, P=0.04) were independently associated with increased risk of RRTIs. Other variables, including cytokine concentrations and NK cell percentages, were not independently associated with RRTIs risk in this model (Figure 4).

Figure 4.

Forest plot of multivariate logistic regression results identifying risk factors for RRTIs following M. pneumoniae infection. Odds ratios and 95% confidence intervals are shown for key predictors, including IgA, CD3⁺ and CD4⁺ T cell levels. CI, confidence interval; Ig, immunoglobulin; MP, Mycoplasma pneumoniae; OR, odds ratio; RRTIs, recurrent respiratory tract infections.

Discussion

In June 2023, a rise in MPP cases was observed, followed by a sharp increase in October–November, drawing global attention, including alerts from the World Health Organization (23). Following the relaxation of non-pharmaceutical interventions, the average incidence of MPP in children increased to 4.12% between April and October 2023 (24). This pattern suggests that M. pneumoniae infection, regardless of disease severity, may preferentially affect the lower respiratory tract, thereby increasing the risk of subsequent recurrence of lower respiratory tract infections (LRTIs). Despite extensive research on M. pneumoniae, the underlying mechanisms and risk factors contributing to severe pneumonia and its complications remain inadequately defined.

In the present study, we comprehensively analyzed the clinical, immunological, and inflammatory profiles of children with M. pneumoniae infection and examined risk factors associated with RRTIs. Children in the severe group demonstrated more pronounced respiratory symptoms—including high fever, persistent cough, dyspnea, and pulmonary rales compared to the mild group, who presented with less severe symptoms and no evidence of respiratory failure. Demographic characteristics, including age and sex, were comparable between groups. Seasonal distribution analysis revealed peak infections during summer months: July and August for mild cases, and June and July for severe cases. This seasonal clustering is consistent with previous epidemiological trends and may reflect environmental or behavioral factors influencing pathogen transmission.

Although asthma was a common complication in both groups (–14.5%), children with mild infection exhibited a broader range of complications, including cough-variant asthma and febrile convulsions. These observations suggest that mild M. pneumoniae infections may contribute to chronic airway hypersensitivity or neurological manifestations, whereas severe infections are more likely to cause acute pulmonary compromise.

Immunological analysis revealed that children who developed RRTIs exhibited a pattern of dysregulated adaptive immune activation, characterized by increased proportions of CD3⁺ and CD4⁺ T cells together with elevated IgA levels. Heatmap analysis demonstrated a distinct immune activation profile in these children, suggesting a tendency toward skewed T-helper cell mediated mucosal immune responses. In contrast, IL-1β levels were relatively lower, indicating a possible imbalance between early innate immune activation and downstream adaptive immune responses. Multivariate logistic regression further identified elevated CD3⁺ and CD4⁺ T-cell proportions as independent predictors of RRTIs. Although IgA levels were higher in children who developed RRTIs, this elevation may reflect persistent mucosal immune stimulation rather than effective protective immunity. As a key component of mucosal defense, IgA plays a critical role in preventing pathogen adherence and colonization at the airway surface. Sustained antigen exposure or incomplete pathogen clearance may induce compensatory IgA production that is functionally insufficient to prevent repeated infections, thereby perpetuating aberrant T-helper cell activation and contributing to chronic airway inflammation or disruption of the respiratory microbiome (25,26).

Recent studies have proposed that DNTs may play a role in immune dysregulation in infectious and autoimmune conditions, including post-viral complications (27-29). Although DNTs were not evaluated in this study, their potential involvement in M. pneumoniae–induced immune alterations—particularly in prolonged or extrapulmonary disease presentations—merits further investigation. Future research should assess whether DNT expansion correlates with disease severity or post-infectious sequelae in pediatric populations.

Children with severe MPP also showed increased T-cell activation that overlapped with the immune profile observed in the RRTI group. This finding suggests that overactive cellular immunity may contribute not only to acute disease severity but also to increased susceptibility to recurrent infections. Longitudinal follow-up revealed that severe M. pneumoniae infection predisposed children to recurrent respiratory symptoms and asthma within one year, likely reflecting persistent immune dysregulation. Consistent with previous studies (21), severe M. pneumoniae infection was associated with a higher incidence of recurrent respiratory symptoms within 1 year. In addition, persistent M. pneumoniae colonization has been described in children with RRTIs (30). Owing to its intracellular persistence and limited susceptibility to immune-mediated clearance, M. pneumoniae may remain difficult to eradicate, thereby perpetuating chronic airway inflammation (31).

In summary, this study identifies a distinct immunophenotypic pattern in children who develop RRTIs following M. pneumoniae infection, characterized by enhanced adaptive immune responses accompanied by relatively attenuated early innate immune activity. These findings support the concept that host immune imbalance, rather than pathogen exposure alone, contributes to post-infectious recurrence. Such immunological markers may provide a basis for future risk stratification and inform early intervention strategies aimed at reducing recurrent respiratory morbidity.

Limitations

This study has several limitations. It was conducted at a single center with a relatively small sample size, which may limit the generalizability of the findings. Co-infections with other respiratory pathogens could not be entirely ruled out, and intrafamilial transmission from asymptomatic carriers was not systematically assessed. Follow-up monitoring of M. pneumoniae colonization and immune parameters was not performed, limiting our ability to evaluate dynamic or long-term immunological changes. Genotyping of M. pneumoniae isolates was also not performed, restricting conclusions regarding potential associations between specific genotypes and disease severity. We acknowledge that RRTIs may have been underestimated if patients sought care at other institutions.

Regarding statistical analysis, recurrence within 12 months was treated as a binary outcome for logistic regression, which is clinically interpretable. However, alternative approaches such as time-to-event modeling (Cox regression) or count models (Poisson or Negative Binomial) could provide additional insight. Some subgroup and cytokine analyses may have been underpowered due to limited sample size and variability in immune parameters. These methodological considerations should be taken into account when interpreting our findings.

The immunological assessment also had inherent constraints. The selected cytokine panel (IL-1β, CCL5, CXCL10, IFN-γ) and immune indicators (immunoglobulins, complement, and lymphocyte subsets) represent only a portion of the complex host immune response. Immune profiling was conducted exclusively during the acute phase of infection, when transient inflammatory responses may influence measurements. Additional potential confounders including environmental exposures, allergic susceptibility, and vaccination history were not systematically collected and could have affected the risk of recurrent infections.

Conclusions

In conclusion, this study provides preliminary evidence that host immune responses, rather than pathogen genotype, may contribute to differences in both disease severity and recurrence following M. pneumoniae infection in children. Although these results should be interpreted with caution due to methodological limitations, they highlight potential immunological risk factors for recurrent disease and underscore the importance of integrating clinical and immune data in risk assessment. Future multicenter, longitudinal studies incorporating extended follow-up, convalescent-phase immune profiling, and broader assessment of clinical and environmental variables are needed to validate and expand upon these findings.

Supplementary

The article’s supplementary files as

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study protocol was reviewed and approved by the Ethics Committee of Chengdu Women and Children’s Central Hospital (approval No. 2023(32)-2), and the study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All clinical data were anonymized prior to analysis to ensure patient confidentiality. Written informed consent to participate was obtained from the parents or legal guardians of all participants under the age of 16.

Data Sharing Statement

Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-825/dss

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DOI: 10.21037/tp-2025-aw-825