Abstract
ABSTRACT
Accumulating evidence suggests a link between the gut microbiota and metabolome and mild cognitive impairment (MCI); however, it remains largely unknown whether these parameters can aid MCI diagnosis and predict disease progression. We comprehensively characterized gut microbiota and metabolomic profiles in fecal samples from 35 individuals, including 16 spousal pairs in which one partner had MCI. Compared with controls, MCI cases exhibited significantly elevated levels of
Izemoplasmatales
spp. in the fecal microbiome and proline in the metabolome, along with decreased levels of
Ruminococcus
spp.,
Lachnospira
spp., and fumaric acid. Among MCI cases, rapid progressors showed significantly increased
Bilophila
spp. in the microbiome, accompanied by higher arginine, aspartic acid, and hypoxanthine in the metabolome, and reduced
Coprococcus
spp., compared with slow progressors. Machine learning models integrating microbiota and metabolite features identified marker sets that accurately distinguished MCI cases from matched cognitively healthy controls (AUC: 0.944) and predicted rapid disease progression, with the integrated model outperforming single‐modality models for diagnosis. These findings identify distinct gut microbiota and metabolite signatures associated with MCI, highlighting their potential as candidate biomarkers for early diagnosis and risk stratification, thereby addressing the unmet need for simple tests for early detection.