Dataset Information

Pediatric Obstructive Sleep Apnea is Associated With Changes in the Oral Microbiome and Urinary Metabolomics Profile: A Pilot Study.

ABSTRACT:

Study objectives

Several cross-sectional studies have reported associations between oral diseases and obstructive sleep apnea (OSA). However, there have been no reports regarding the structure and composition of the oral microbiota with simultaneous evaluation of potential associations with perturbed metabolic profiles in pediatric OSA.

Methods

An integrated approach, combining metagenomics based on high-throughput 16S rRNA gene sequencing, and metabolomics based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry, was used to evaluate the oral microbiome and the urinary metabolome.

Results

16S rRNA gene sequencing indicated that the oral microbiome composition was significantly perturbed in pediatric OSA compared with normal controls, especially with regard to Firmicutes, Proteobacteria, Bacteroidetes, Fusobacteria, and Actinobacteria. Moreover, metabolomics profiling indicated that 57 metabolites, 5 of which were metabolites related to the microflora of the digestive tract, were differentially present in the urine of pediatric patients with OSA and controls. Co-inertia and correlation analyses revealed that several oral microbiome changes were correlated with urinary metabolite perturbations in pediatric OSA. However, this correlation relationship does not imply causality.

Conclusions

High-throughput sequencing revealed that the oral microbiome composition and function were significantly altered in pediatric OSA. Further studies are needed to confirm and determine the mechanisms underlying these findings.

SUBMITTER: Xu H

PROVIDER: S-EPMC6134247 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Pediatric Obstructive Sleep Apnea is Associated With Changes in the Oral Microbiome and Urinary Metabolomics Profile: A Pilot Study.

Xu Huajun H Li Xiaoyan X Zheng Xiaojiao X Xia Yunyan Y Fu Yiqun Y Li Xinyi X Qian Yingjun Y Zou Jianyin J Zhao Aihua A Guan Jian J Gu Meizhen M Yi Hongliang H Jia Wei W Yin Shankai S

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 20180915 9

<h4>Study objectives</h4>Several cross-sectional studies have reported associations between oral diseases and obstructive sleep apnea (OSA). However, there have been no reports regarding the structure and composition of the oral microbiota with simultaneous evaluation of potential associations with perturbed metabolic profiles in pediatric OSA.<h4>Methods</h4>An integrated approach, combining metagenomics based on high-throughput 16S rRNA gene sequencing, and metabolomics based on ultra-performa ...[more]

PMID: 30176961

Similar Datasets

Project description:IntroductionDiagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists.MethodWe used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT.ResultsFrom the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm.ConclusionTonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist.

Project description:Study objectivesTo explore and analyze diversity and abundance of oropharyngeal microbiota in patients with obstructive sleep apnea (OSA).MethodsThis was a cross-sectional study. Middle-aged men, suspected to have OSA, referred to full-night polysomnography, and willing to provide oropharyngeal swab samples, were consecutively enrolled. OSA severity was assessed by apnea-hypopnea index (AHI) as non-OSA (AHI < 5 events/h) and OSA (AHI ≥ 15 events/h). Bacterial DNA of oropharyngeal samples was extracted and quality test performed. Oropharyngeal microbiota was analyzed using 16S ribosomal DNA (rDNA) sequencing, and bioinformatic analysis carried out after sequencing.ResultsSamples from 51 men (25 in the non-OSA group and 26 in the OSA group) were sent for examination. Of these, 40 samples were found to have sufficient concentration of DNA and were analyzed for bioinformatics. In alpha diversity analysis, the OSA group exhibited significantly lower sobs (198.33 ± 21.71 versus 216.57 ± 26.21, P = .022), chao (221.30 ± 26.62 versus 243.86 ± 26.20, P = .014), ace (222.17 ± 27.15 versus 242.42 ± 25.81, P = .028) and shannon index (3.14 ± 0.23 versus 3.31 ± 0.26, P = .035), suggesting a reduction in microbial species diversity. We further divided participants into non-OSA, moderate OSA, and severe OSA groups and observed a significant decrease in the bacterial biodiversity of OSA groups compared with the non-OSA group, with the most significant decrease occurring in the moderate OSA group. Principal coordinate analysis showed two extremely different oropharyngeal microbial communities in non-OSA and OSA groups. More interestingly, proportion of Neisseria was slightly higher in the severe OSA group (20.64%), followed by the moderate OSA and non-OSA groups (12.57% and 9.69%, respectively). Glaciecola was not detected in the OSA groups compared to the non-OSA group (0 versus 0.772 ± 0.4754, P < .001).ConclusionsMiddle-aged men with OSA showed less oropharyngeal species diversity and altered abundance, on which further confirmation is warranted.

Dataset Information

Pediatric Obstructive Sleep Apnea is Associated With Changes in the Oral Microbiome and Urinary Metabolomics Profile: A Pilot Study.

Study objectives

Methods

Results

Conclusions

Publications

Pediatric Obstructive Sleep Apnea is Associated With Changes in the Oral Microbiome and Urinary Metabolomics Profile: A Pilot Study.

Similar Datasets

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