Journal of psychosomatic research · 2025
Beyond the surface: gut microbiome and implicit learning in anorexia nervosa - A pilot study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Kipper Jasmin Anastasia, Wiener Markus, Horvath Angela, Haidacher Fiona, Lackner Sonja, Holasek Sandra, Ramirez-Obermayer Anna, Bengesser Susanne, Baranyi Andreas, Lahousen-Luxenberger Theresa, Leitner Manuel, Butler Mary I, Wagner-Skacel Jolana, Mörkl Sabrina
Аннотация
BACKGROUND: Anorexia nervosa (AN) is characterized by high mortality, limited treatment options, altered gut microbiome composition, and impaired feedback (FB) learning. This pilot study explores the relationship between gut microbiome composition and reduced implicit FB learning in AN.
METHODS: Sixteen female patients with AN and fourteen healthy controls (HC) completed the Weather Prediction Task to assess implicit learning (feedback and paired-associate formats). Gut microbiome composition was analyzed via Illumina sequencing, with species differences identified using linear discriminant effect size (LEfSe) analysis.
RESULTS: Patients with AN had lower FB learning scores compared to HC (p = .012), while no significant difference in the paired-associate task was found (p = .905). Better feedback learning was associated with greater gut microbiome diversity, indicated by a larger number of observed species (r = 0.45, p = .013), and a higher Shannon index (r = 0.39, p = .035). LEfSe analysis revealed an increased abundance of Akkermansia muciniphila in AN, while short-chain fatty acid-producing species (such as phylum Firmicutes, including Roseburia, Agathobacter and Faecalibacterium) were more prevalent in HC. Participants with higher FB learning exhibited an increase in Actinobacteria, particularly Bifidobacteria, while those with low FB learning had an increase in the Lachnospirales of the phylum Firmicutes.
CONCLUSIONS: These findings highlight the gut microbiome's potential role in implicit learning in AN, which could play an important role in the success of treatments. Future research should focus on microbiome-targeted interventions, such as probiotics, particularly Bifidobacteria, to improve gut health and implicit learning in patients with AN.
METHODS: Sixteen female patients with AN and fourteen healthy controls (HC) completed the Weather Prediction Task to assess implicit learning (feedback and paired-associate formats). Gut microbiome composition was analyzed via Illumina sequencing, with species differences identified using linear discriminant effect size (LEfSe) analysis.
RESULTS: Patients with AN had lower FB learning scores compared to HC (p = .012), while no significant difference in the paired-associate task was found (p = .905). Better feedback learning was associated with greater gut microbiome diversity, indicated by a larger number of observed species (r = 0.45, p = .013), and a higher Shannon index (r = 0.39, p = .035). LEfSe analysis revealed an increased abundance of Akkermansia muciniphila in AN, while short-chain fatty acid-producing species (such as phylum Firmicutes, including Roseburia, Agathobacter and Faecalibacterium) were more prevalent in HC. Participants with higher FB learning exhibited an increase in Actinobacteria, particularly Bifidobacteria, while those with low FB learning had an increase in the Lachnospirales of the phylum Firmicutes.
CONCLUSIONS: These findings highlight the gut microbiome's potential role in implicit learning in AN, which could play an important role in the success of treatments. Future research should focus on microbiome-targeted interventions, such as probiotics, particularly Bifidobacteria, to improve gut health and implicit learning in patients with AN.