Department of Endocrinology and Metabolic Diseases, Istanbul University-Cerrahpasa, Cerrahpasa School of Medicine, Istanbul, Turkey.
Department of Microbiology and Clinical Microbiology, School of Medicine, Erciyes University, Kayseri, Turkey.
Pituitary. 2022 Jun;25(3):520-530. doi: 10.1007/s11102-022-01223-1. Epub 2022 Apr 25.
Our aim was to investigate the changes in the composition of oral and gut microbiota in patients with newly diagnosed acromegaly and their relationship with IGF-1 levels.
Oral and fecal samples were collected from patients with newly diagnosed acromegaly without comorbidities and from healthy controls. The composition of the microbiota was analyzed. The general characteristics, oral and stool samples of the patients and healthy control subjects were compared. The changes in microbiota composition in both habitats, their correlations and associations with IGF-1 were statistically observed using machine learning models.
Fifteen patients with newly diagnosed acromegaly without comorbidities and 15 healthy controls were included in the study. There was good agreement between fecal and oral microbiota in patients with acromegaly (p = 0.03). Oral microbiota diversity was significantly increased in patients with acromegaly (p < 0.01). In the fecal microbiota, the Firmicutes/Bacteroidetes ratio was lower in patients with acromegaly than in healthy controls (p = 0.011). Application of the transfer learned model to the pattern of microbiota allowed us to identify the patients with acromegaly with perfect accuracy.
Patients with acromegaly have their own oral and gut microbiota even if they do not have acromegaly-related complications. Moreover, the excess IGF-1 levels could be correctly predicted based on the pattern of the microbiome.
我们旨在研究新诊断为肢端肥大症患者口腔和肠道微生物群组成的变化及其与 IGF-1 水平的关系。
收集无合并症的新诊断肢端肥大症患者和健康对照者的口腔和粪便样本。分析微生物群的组成。比较患者和健康对照者的一般特征、口腔和粪便样本。使用机器学习模型对两种生境中微生物群组成的变化、它们之间的相关性以及与 IGF-1 的关联进行统计学观察。
本研究纳入了 15 例无合并症的新诊断肢端肥大症患者和 15 例健康对照者。肢端肥大症患者的粪便和口腔微生物群之间具有良好的一致性（p=0.03）。肢端肥大症患者口腔微生物群的多样性显著增加（p<0.01）。在粪便微生物群中，肢端肥大症患者的厚壁菌门/拟杆菌门比值低于健康对照组（p=0.011）。应用转移学习模型对微生物群模式进行分析，可准确识别肢端肥大症患者。
即使没有肢端肥大症相关并发症，肢端肥大症患者也有其自身的口腔和肠道微生物群。此外，基于微生物组的模式可以正确预测 IGF-1 水平的升高。
