[Efficacy of weight management combined with uvulopalatopharyngoplasty for obesityrelated obstructive sleep apnea-hypopnea syndrome]

Artificial Intelligence Screening Tool for Obstructive Sleep Apnoea: A Study Based on Outpatients at a Sleep Medical Centre

Purpose

Due to the lack of clear screening guidelines for different populations, identify strategies for obstructive sleep apnea (OSA) in the outpatient population are unclear, a large number of potential OSA outpatients have not been identified in time. The purpose of our study was to evaluate the applicability and accuracy of artificial intelligence sleep screening in outpatients and to provide a reference for OSA screening in different populations.

Methods

A type IV wearable artificial intelligence sleep monitoring (AISM) device was used to screen adults in the sleep clinic of the Sleep Medical Center for OSA screening, and the general demographic data of the patients were collected. The epidemiological characteristics obtained by AISM screening were analysed. The accuracy of the AISM for the diagnosis of OSA was evaluated and compared with that of polysomnography (PSG).

Results

A total of 1492 participants completed all the studies. The data included 1448 cases total, including 1096 male patients and 352 female patients, with 620 of the total patients being overweight (42.82%) and 429 being obese patients (29.63%). The prevalence of males was 78.19%, and that of females was 55.97% (χ2 = 95.72, P < 0.001). In males, the risk of moderate to severe OSA was 74.21% in obese people, while in females, the risk was 50%. Age, body mass index (BMI) and the oxygen desaturation index (ODI) were positively correlated and negatively correlated with the lowest and mean oxygen saturation. A total of 100 participants completed both PSG and AISM monitoring, and the accuracies of the AISM in diagnosing mild and moderate-to-severe OSA were 94% and 98%, respectively.

Conclusion

The AISM exhibits good accuracy, and the use of an objective and convenient sleep detection device to screen a large sample population of outpatients is feasible. The prevalence of OSA in adults in sleep clinics is high, and age, sex, and BMI are risk factors for OSA.

[Analysis of gut target microbiota and species difference in patients with obstructive sleep apnea based on 16S rRNA sequencing]

OBJECTIVE

To explore the difference in gut microbiota composition between patients with obstructive sleep apnea (OSA) and healthy individuals and the role of gut microbiota in the pathogenesis of OSA.

METHODS

Thirty-nine patients with OSA admitted to our hospital between May and December, 2022 and 20 healthy individuals were enrolled in this study. Stool samples were collected from all the participants for analysis of microbiome composition using 16S rRNA high- throughput sequencing analysis. The alpha diversity, beta diversity, and species difference were determined between the two groups and marker species analysis and metabolic pathway function prediction analysis were performed.

RESULTS

The species diversity (Shannon and Simpson) indexes, richness (observed species) and evenness (Pielou) of gut microbiota were significantly lower in OSA patients than in the healthy individuals (P < 0.05). The OSA patients had also a significantly lowered community diversity (P < 0.05) with different gut microbial communities from those of the healthy individuals shown by increased relative abundance of potentially pathogenic bacteria such as Pseudomonas and Monocytogenes (P < 0.05). LEfSe analysis showed that the abundance of 23 species of gut microbiota differed significantly between the two groups and the OSA patients had significant increases in the abundance of Pseudomonas, Meganomonas, and Fusobacterium (P < 0.05). The differential marker flora affected host homeostasis. Random Forest and ROC curve analyses confirmed that Pseudomonas could be used as important biomarkers for a differential diagnosis. Metabolic pathway function prediction analysis showed that biosynthesis function had the greatest contribution to maintaining gut microbiota homeostasis, and Pseudomonas affected the occurrence and progression of OSA by participating in aromatic bioamine degradation and ketogluconic acid metabolic pathway.

CONCLUSION

OSA patients have obvious gut microbiota disturbances, and Pseudomonas may affect the development of OSA by participating in substance metabolism to serve as the potential target gut bacteria for OSA treatment.