Abstract
ObjectTo clarify the clinical efficacy of washed microbiota transplantation (WMT) for metabolic syndrome (MetS), and explore the differences in the metabolic profile of bacterial outer membrane vesicles (OMVs) in donor fecal bacteria suspension received by MetS patients with good and poor outcomes, and to construct a predictive model for the efficacy of WMT for MetS using differential metabolites.MethodsMedical data 65 MetS patients who had completed at least 2 courses of WMT from 2017.05 to 2023.07 were collected. Fecal bacteria suspension of WMT donors were collected, and the clinical data of MetS patients treated with WMT during this period were collected as well. The changes of BMI, blood glucose, blood lipids, blood pressure and other indicators before and after WMT were compared. OMVs were isolated from donor fecal bacteria suspension and off-target metabolomic sequencing was performed by Liquid Chromatograph Mass Spectrometer (LC–MS).ResultsCompared with baseline, Body mass index (BMI), Systolic blood pressure (SBP) and Diastolic blood pressure (DBP) of MetS patients showed significant decreases after the 1st (short-term) and 2nd (medium-term) courses, and fasting blood glucose (FBG) also showed significant decreases after the 1st session. There was a significant difference between the Marked Response OMVs and the Moderate Response OMVs. It was showed that 960 metabolites were significantly up-regulated in Marked Response OMVs and 439 metabolites that were significantly down-regulated. The ROC model suggested that 9-carboxymethoxymethylguanine, AUC = 0.8127, 95% CI [0.6885, 0.9369], was the most potent metabolite predicting the most available metabolite for efficacy.ConclusionWMT had significant short-term and medium-term clinical efficacy in MetS. There were differences in the structure of metabolites between Marked Response OMVs and Moderate Response OMVs. The level of 9-Carboxy methoxy methylguanine in Marked Response OMVs can be a good predictor of the efficacy of WMT in the treatment of MetS.
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