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The emergent recognition of the gut-brain axis connection has shed light on the role of the microbiota in modulating the gut-brain axis's functions. Several microbial metabolites, such as serotonin, kynurenine, tryptamine, indole, and their derivatives originating from tryptophan metabolism have been implicated in influencing this axis. In our study, we aimed to investigate the impact of running exercises on microbial tryptophan metabolism using a mouse model. We conducted a multi-omics analysis to obtain a comprehensive insight into the changes in tryptophan metabolism along the microbiota-gut-brain axis induced by running exercises. The analyses integrated multiple components, such as tryptophan changes and metabolite levels in the gut, blood, hippocampus, and brainstem. Fecal microbiota analysis aimed to examine the composition and diversity of the gut microbiota, and taxon-function analysis explored the associations between specific microbial taxa and functional activities in tryptophan metabolism. Our findings revealed significant alterations in tryptophan metabolism across multiple sites, including the gut, blood, hippocampus, and brainstem. The outcomes indicate a shift in microbiota diversity and tryptophan metabolizing capabilities within the running group, linked to increased tryptophan transportation to the hippocampus and brainstem through circulation. Moreover, the symbiotic association between Romboutsia and A. muciniphila indicated their potential contribution to modifying the gut microenvironment and influencing tryptophan transport to the hippocampus and brainstem. These findings have potential applications for developing microbiota-based approaches in the context of exercise for neurological diseases, especially on mental health and overall well-being.
RESUMEN
INTRODUCTION: Modifiable hemodialysis treatment parameters may impact patient reported outcomes, including recovery time. Answers to the recovery question may predict the impact of treatment parameters on clinical outcomes and health related quality of life. However, the reliability of answers to the recovery question after consecutive and nonconsecutive dialysis treatments in diverse populations has not been established. OBJECTIVE: To assess the reliability of this instrument and to determine if recovery time was associated with modifiable dialysis parameters, we conducted a quality assurance project in which we asked, "How long did it take you to recover from your last dialysis session?" after consecutive and nonconsecutive treatments. METHODS: We asked patients the recovery question ≤ seven times. We computed polychoric correlations to assess within patient correlations. We used random intercept ordinal logistic regression models to test for associations of recovery time with patient variables. RESULTS: We obtained answers to the recovery question in association with 1572 treatments in 364 patients. Recovery time was <2 hours in 52.1%; 2 to 7 hours in 20.9%; and >7 hours in 27.0% of treatments. Polychoric correlations demonstrated highly reliable responses within individual patients between consecutive and nonconsecutive treatments. Prolonged recovery was associated with a dialysate potassium of 1 vs. 2 mEq/L (odds ratio [OR] 2.25 {95% confidence interval [CI] 1.43-3.55}) and 1 vs. 3 mEq/L (OR 1.88 [95% CI 1.06-3.33]); vintage >6 months (OR 2.43 [95% CI 1.42-4.16]), body mass index >35 kg/m2 (OR 1.94 [95% CI 1.18-3.20]), post-dialysis systolic blood pressure (SBP) <115 mmHg (OR 1.57 [95% CI 1.04-2.37]) and intradialytic cramps (OR 1.76 [95% CI 1.09-2.86]). There were no associations with gender, race, age, ESRD etiology, intradialytic SBP <90 mmHg, serum sodium or potassium, dialysate sodium, bicarbonate or temperature, blood flow rate, or ultrafiltration rate. CONCLUSIONS: Responses to the recovery question were reliable and low dialysate potassium was associated with prolonged recovery.