Evidence map›Paper›PMID 41461980›Full record

ArticlePituitary2025

Oral and gut microbiota after acromegaly treatment: prospective assessment and insights from machine learning.

Aysa Hacioglu, Zuleyha Karaca, Emre Urhan, Ahmet Numan Demir, Serdar Sahin, Aycan Gundogdu, Mehmet Hora, Ozkan Ufuk Nalbantoglu, Sukru Oral, Necmettin Tanriover and 5 more

Abstract read
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In one paragraph

Article in Pituitary, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Aysa HaciogluDepartment of Endocrinology and Metabolic Diseases, Erciyes University Faculty of Medicine, Kayseri, Turkey.
Zuleyha KaracaDepartment of Endocrinology and Metabolic Diseases, Erciyes University Faculty of Medicine, Kayseri, Turkey.
Emre UrhanDepartment of Endocrinology and Metabolic Diseases, Erciyes University Faculty of Medicine, Kayseri, Turkey.
Ahmet Numan DemirDepartment of Endocrinology and Metabolic Diseases, Cerrahpasa Faculty of Medicine, Istanbul University- Cerrahpasa, Istanbul, Turkey.
Serdar SahinDepartment of Endocrinology and Metabolic Diseases, Cerrahpasa Faculty of Medicine, Istanbul University- Cerrahpasa, Istanbul, Turkey.
Aycan GundogduDepartment of Microbiology and Clinical Microbiology, School of Medicine, Erciyes University, Kayseri, Turkey.
Mehmet HoraGenome and Stem Cell Center (GenKok), Erciyes University, Kayseri, Turkey.
Ozkan Ufuk NalbantogluGenome and Stem Cell Center (GenKok), Erciyes University, Kayseri, Turkey.
Sukru OralDepartment of Neurosurgery, Erciyes University School of Medicine, Kayseri, Turkey.
Necmettin TanrioverDepartment of Neurosurgery, Istanbul University-Cerrahpasa, Istanbul, Turkey.
İzzet ÖkçesizDepartment of Radiology, Erciyes University Faculty of Medicine, Kayseri, Turkey.
Hatice Sebile DokmetasDepartment of Endocrinology, Cemil Tascıoglu City Hospital, University of Health Sciences, Istanbul, Turkey.
Pinar KadıogluDepartment of Endocrinology and Metabolic Diseases, Cerrahpasa Faculty of Medicine, Istanbul University- Cerrahpasa, Istanbul, Turkey.
Kursad UnluhizarciDepartment of Endocrinology and Metabolic Diseases, Erciyes University Faculty of Medicine, Kayseri, Turkey.
Fahrettin KelestimurDepartment of Endocrinology, Yeditepe University Faculty of Medicine, Istanbul, Turkey. fahrettin.kelestemur@yeditepe.edu.tr.ORCID http://orcid.org/0000-0002-2861-4683

Funding

Bilimsel Araştırma Projeleri, Erciyes Üniversitesi TSA-2022-12219
6 · The paper itself

Abstract

purposeThe human endocrine system and microbiota interact bidirectionally. Patients with acromegaly have distinct oral and gut microbiota profiles. The study aims to investigate the effects of acromegaly treatment on oral and fecal microbiota and evaluate their associations with insulin-like growth factor-1 (IGF-1) normalization. The predictive value of baseline microbiota-based machine learning algorithms regarding treatment outcomes is also analyzed.

methodsOral and fecal microbiota samples were prospectively collected from newly diagnosed acromegaly patients before and one year post-treatment. Following DNA isolation 16 S rRNA sequencing was performed, and bioinformatic analyses were conducted.

resultsA total of 19 patients were included (10 female(52.6%); mean age 48.8 ± 12.1 years). Seven patients achieved remission with surgery alone (Group 1), seven with combined surgical and medical treatment (Group 2), while five did not achieve remission (Group 3). Alpha and beta diversities were similar but microbial compositions differed significantly among the groups. In prospective analyses of Group 1 and combined Groups 1 and 2, microbiota profiles changed significantly. In Group 1, decrease in IGF-1 levels correlated positively with oral Succinivibrio. The developed classification model, using baseline microbiota profiles, accurately distinguished between the groups, and identified patients who achieved complete remission after surgery alone.

conclusionOral and fecal microbiota compositions in patients with acromegaly significantly change with treatment modalities and remission status with some taxa correlating with IGF-1 normalization. The findings provide preliminary evidence that microbiota may help predict treatment response. Further studies with larger patient populations are needed to validate the results.

Indexed as

AcromegalyGastrointestinal MicrobiomeMachine LearningAdultFecesFemaleHumansInsulin-Like Growth Factor IMaleMiddle AgedProspective StudiesInsulin-Like Growth Factor IAcromegalyGrowth hormoneIGF-1MicrobiotaSomatostatin receptor ligand

Identifiers

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.