Serdar Sahin1, Aycan Gundogdu2,3, Ufuk Nalbantoglu3,4, Pinar Kadioglu1, Zuleyha Karaca5, Aysa Hacioglu5, Muhammed Emre Urhan5, Kursad Unluhizarci5, Ahmet Numan Demir1, Mehmet Hora3, Emre Durcan1, Gülsah Elbüken6, Hatice Sebile Dokmetas7, Sayid Shafi Zuhur6, Fahrettin Kelestimur8. 1. Department of Endocrinology and Metabolic Diseases, Istanbul University-Cerrahpasa, Cerrahpasa School of Medicine, Istanbul, Turkey. 2. Department of Microbiology and Clinical Microbiology, School of Medicine, Erciyes University, Kayseri, Turkey. 3. Genome and Stem Cell Center (GenKok), Erciyes University, Kayseri, Turkey. 4. Department of Computer Engineering, Erciyes University, Kayseri, Turkey. 5. Department of Endocrinology and Metabolic Diseases, School of Medicine, Erciyes University, Kayseri, Turkey. 6. Department of Endocrinology and Metabolic Diseases, School of Medicine, Namik Kemal University, Tekirdaǧ, Turkey. 7. Department of Endocrinology and Metabolic Diseases, School of Medicine, Istanbul Medipol University, Istanbul, Turkey. 8. Department of Endocrinology and Metabolic Diseases, School of Medicine, Yeditepe University, Istanbul, Turkey. fktimur@erciyes.edu.tr.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Elizabeth A Jensen; Jonathan A Young; Zachary Jackson; Joshua Busken; Jaycie Kuhn; Maria Onusko; Ronan K Carroll; Edward O List; J Mark Brown; John J Kopchick; Erin R Murphy; Darlene E Berryman Journal: Endocrinology Date: 2022-07-01 Impact factor: 5.051