The Microbiome as a Mirror: Distinguishing Healthy Individuals From Chronic Constipated Patients and Identifying Those With Hidden Psychiatric Burden

Background Chronic constipation (CC) frequently co‐occurs with psychiatric distress (PD), complicating management. The gut microbiota is implicated in CC and the gut–brain axis, but whether fecal signatures can identify CC and stratify patients by psychiatric status remains unclear. This study aimed to develop and internally/externally validate two separate microbiota‐based models: one discriminating patients with CC from healthy controls (HCs) and another identifying CC patients with PD (CPD) versus those without (CNPD). Methods We curated gut microbiota profiles (16S rRNA) from public datasets (discovery cohort, n = 478) and our institutional cohort (external validation, n = 45). After quality control, taxonomic profiles and functional metagenomes (PICRUSt2) were generated. Differentially abundant genera and pathways were identified, with functional enrichment analysis (taxon set enrichment analysis). A two‐step machine learning approach was employed: (1) a support vector machine (SVM) classifier distinguishing CC from HCs and (2) an Elastic Net classifier differentiating CPD from CNPD within patients with CC. Model performance was evaluated in the validation cohort using the area under the receiver operating characteristic curve (AUC), decision curve analysis, and clinical impact curves. Results Alterations in gut microbial composition were observed across the groups. Compared with HCs, patients with CC exhibited reduced alpha diversity and distinct beta‐diversity clustering, characterized by depletion of short‐chain fatty acid (SCFA)‐producing genera (e.g., Faecalibacterium and Roseburia ) and enrichment of potentially pathogenic genera (e.g., Methanomassiliicoccus ). Importantly, within patients with CC, the CPD subgroup showed further deviation from CNPD, including a specific reduction in Lachnospiraceae_ND3007_group and an increased abundance of Methanosphaera . The SVM model achieved a modest AUC of 0.71 for discriminating CC from HC, indicating limited discriminative capacity. The Elastic Net model demonstrated a lower but important discriminatory ability (AUC = 0.67) for identifying PD within patients with CC. Conclusion This study provides proof of concept that gut microbiota‐based models can distinguish patients with CC from healthy individuals with modest accuracy. Identifying PD within patients with CC has proven more challenging, and the current models do not meet the performance standards required for clinical decision‐making (e.g., AUC ≥ 0.80 for screening tools). Nevertheless, the microbial signatures, particularly taxa linked to neurotransmitter regulation and inflammation, support gut–brain axis involvement. These findings may serve as a risk indicator, but future improvements integrating clinical and metabolomic data are needed before any clinical application can be considered.