アクロメガリー治療後の口腔内および腸内細菌叢：前向き評価と機械学習からの洞察

The 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.

Oral 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.

A 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.

Oral 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.