Dataset Information

Increasing Age Affected Polymorphonuclear Neutrophils in Prognosis of Mycoplasma pneumoniae Pneumonia.

ABSTRACT:

Purpose

It is well known that age is related to the incidence of Mycoplasma pneumoniae pneumonia (MPP), and how age and other factors contribute to MPP remains unclear. In this study, we investigate how age affects the prognosis of MPP.

Patients and methods

A total number of 1875 hospitalized children with pneumonia were enrolled in this study, including 52 children with refractory M. pneumoniae pneumonia (RMPP) and 298 children with non-RMPP. We used multiple logistic regression analysis to further identify the risk factors of RMPP, and found that age and polymorphonuclear neutrophils (PMNs) count were the key independent risk factors for the occurrence of RMPP. In order to improve specificity, 4.5 years old was taken as the cut-off value. Then, according to the cut-off value of age, 76 participants were recruited and divided into four groups: <4.5y MPP group, ≥4.5y MPP group, <4.5y health control (<4.5yHC) and ≥4.5y HC group. We explored the diverse functions of primary PMNs from children of different ages with MPP at cellular level. Besides, we studied the relationship between lung injury and PMNs in mice model with MPP of different ages.

Results

We found that the age and PMNs count of RMPP group were significantly higher than those of the non-RMPP group. Importantly, there is a linear correlation between the age of patients with RMPP and the percentage of PMNs. Further analysis showed that elderly patients infected with M. pneumoniae had more active PMNs function. Meanwhile, proteomics showed that children with M. pneumoniae infection in different age groups have differences in PMNs apoptosis, nicotinamide adenine dinucleotide phosphate, mitochondrial function and oxidative stress. Finally, we found that age is also involved in the pathogenesis of mouse model with MPP.

Conclusion

We speculate that age may contribute to the development of RMPP.

SUBMITTER: Zhao Q

PROVIDER: S-EPMC8374848 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMycoplasma pneumoniae (M. pneumoniae) is an important pathogen of community-acquired pneumonia in children. The factors contributing to the severity of illness caused by M. pneumoniae infection are still under investigation. We aimed to evaluate the sensitivity of common M. pneumoniae detection methods, as well as to analyze the clinical manifestations, genotypes, macrolide resistance, respiratory microenvironment, and their relationship with the severity of illness in children with M. pneumoniae pneumonia in Wuhan.ResultsAmong 1,259 clinical samples, 461 samples were positive for M. pneumoniae via quantitative polymerase chain reaction (qPCR). Furthermore, we found that while serological testing is not highly sensitive in detecting M. pneumoniae infection, but it may serve as an indicator for predicting severe cases. We successfully identified the adhesin P1 (P1) genotypes of 127 samples based on metagenomic and Sanger sequencing, with P1-type 1 (113/127, 88.98%) being the dominant genotype. No significant difference in pathogenicity was observed among different genotypes. The macrolide resistance rate of M. pneumoniae isolates was 96% (48/50) and all mutations were A2063G in domain V of 23S rRNA gene. There was no significant difference between the upper respiratory microbiome of patients with mild and severe symptoms.ConclusionsDuring the period of this study, the main circulating M. pneumoniae was P1-type 1, with a resistance rate of 96%. Key findings include the efficacy of qPCR in detecting M. pneumoniae, the potential of IgM titers exceeding 1:160 as indicators for illness severity, and the lack of a direct correlation between disease severity and genotypic characteristics or respiratory microenvironment. This study is the first to characterize the epidemic and genomic features of M. pneumoniae in Wuhan after the COVID-19 outbreak in 2020, which provides a scientific data basis for monitoring and infection prevention and control of M. pneumoniae in the post-pandemic era.

Project description:BackgroudThe current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP.MethodsA total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA).ResultsMultivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08-1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20-1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23-0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16-2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38-11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram.ConclusionThis study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention.