Lv Yunjiao, Xian Yongtao, Lei Xinye, Xie Siqi, Zhang Biyun
Department of First Clinical College, Guangzhou Medical University, Guangzhou, China.
Department of Pediatrics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Medicine (Baltimore). 2024 Dec 13;103(50):e40900. doi: 10.1097/MD.0000000000040900.
Pediatric obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated with significant neurocognitive and behavioral impairments. Recent studies have highlighted the role of gut microbiota and the microbiota-gut-brain axis (MGBA) in influencing cognitive health in children with OSA. This narrative review aims to summarize current knowledge on the relationship between gut microbiota, MGBA, and cognitive function in pediatric OSA. It also explores the potential of artificial intelligence and machine learning in advancing this field and identifying novel therapeutic strategies. Pediatric OSA is associated with gut dysbiosis, reduced microbial diversity, and metabolic disruptions. MGBA mechanisms, such as endocrine, immune, and neural pathways, link gut microbiota to cognitive outcomes. Artificial intelligence and machine learning methodologies offer promising tools to uncover microbial markers and mechanisms associated with cognitive deficits in OSA. Future research should focus on validating these findings through clinical trials and developing personalized therapeutic approaches targeting the gut microbiota.
小儿阻塞性睡眠呼吸暂停(OSA)是一种常见的与睡眠相关的呼吸障碍,与显著的神经认知和行为损害有关。最近的研究强调了肠道微生物群和微生物群-肠道-脑轴(MGBA)在影响OSA儿童认知健康方面的作用。本叙述性综述旨在总结目前关于小儿OSA中肠道微生物群、MGBA和认知功能之间关系的知识。它还探讨了人工智能和机器学习在推进该领域研究以及确定新治疗策略方面的潜力。小儿OSA与肠道菌群失调、微生物多样性降低和代谢紊乱有关。MGBA机制,如内分泌、免疫和神经通路,将肠道微生物群与认知结果联系起来。人工智能和机器学习方法提供了有前景的工具,以揭示与OSA认知缺陷相关的微生物标志物和机制。未来的研究应专注于通过临床试验验证这些发现,并开发针对肠道微生物群的个性化治疗方法。