Differential Profiles of Gut Microbiota-Derived Metabolites of Bile Acids and Propionate as Potential Predictors of Depressive Disorder in Women with Morbid Obesity at High Risk of Metabolic Dysfunction-Associated Steatotic Liver Disease-A Pilot Study

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a liver condition linked to cardiometabolic diseases and mental health issues, with studies highlighting disruptions in gut microbiota activity, including bile acid (BA) metabolism. Therefore, the main aim of this exploratory analysis was to assess microbiota-derived metabolites, specifically BAs and short-chain fatty acids (SCFAs), as potential biomarkers of depressive disorder (DD) in women with morbid obesity at MASLD risk. In this pilot study, 33 females with morbid obesity who were scheduled for bariatric surgery were evaluated. Medical and clinical data were collected, and microbial metabolites from pre-surgery blood samples were analyzed. Patients were stratified according to the presence of DD. Analysis with Spearman's rank test was used to assess correlations and logistic regression models were built to evaluate biomarkers as predictors of DD risk using both receiver operating characteristic (ROC) and precision-recall curves. In this cohort, 30.3% of females were reported to have DD, in addition to significantly elevated levels of certain BAs and SCFAs, including glycodeoxycholic acid (GDCA) and propionate, which were also correlated with some metabolic biomarkers. However, there were no differences in the incidence of MASLD or metabolic syndrome between patients with DD or without. In conclusion, microbiota-derived metabolites such as GDCA and propionate may influence DD risk in females with morbid obesity; however, their potential use as predictive biomarkers should be further investigated to confirm their role in psycho-metabolic conditions.

Keywords: bile acids; depression; metabolic dysfunction-associated steatotic liver disease; microbial metabolites; obesity.

Figure 1

Significant changes in levels of microbiota-derived metabolites (A) GCA, (B) GDCA, and (C) propionate between control group (CN) and depression disorder cohort (DD). p values were calculated by using ANCOVA, adjusting for type 2 diabetes diagnosis, and by multiple comparisons with Benjamini–Hochberg method. p < 0.05 was considered statistically significant.

Figure 2

Partial correlations between bile acids (BAs), short-chain fatty acids (SCFAs), and other microbial bioactives and the clinical and biochemical characteristics of the study cohort (using diabetes mellitus as a covariate). Spearman’s rank coefficient is displayed only in the cases of significant (p value < 0.05) associations. CDCA, Chenodeoxycholic acid; DCA, Deoxycholic acid; GCDCA, Glycochenodeoxycholic acid; GCA, Glycocholic acid; GDCA, Glycodeoxycholic acid; TCLA, Taurochenodeoxycholic acid; TCDCA, Taurochenodeoxycholic acid; TDCA, Taurodeoxycholic acid; TUDCA, Tauroursodeoxycholic acid; GLCA, Glycolithocholic acid; GUDCA, Glycoursodeoxycholic acid; TMA, Trimethylamine; TMAO, Trimethylamine N-oxide; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HOMA1-IR, homeostatic model assessment method–insulin resistance; HbA1c, glycosylated hemoglobin; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyltransferase; ALP, alkaline phosphatase; LDH, Lactate Dehydrogenase; CRP, C-reactive protein. All BAs are measured as nM; SCFAs are measured as ng/mL;TMA determined as nM. Choline, TMAO, and betaine are measured as uM.

Figure 3

ROC curve and precision–recall curve for microbial metabolites. (A) ROC curve for glycodeoxycholic acid (GDCA), the bile acid (BAs) selected by the best-fit method, in a logistic regression model predicting depression disorder (DD) in a cohort of females with morbid obesity. (B) Precision–recall curve for glycodeoxycholic acid (GDCA), the bile acid (BAs) selected by the best-fit method, in a logistic regression model predicting depression disorder (DD) in a cohort of females with morbid obesity. The dashed diagonal line in the ROC plot represents the performance of a random classifier (AUC = 0.5).

Figure 4

ROC curve and precision–recall curve for propionate. (A) ROC curve for propionate, the predictor selected by the best-fit method among all measured variables in a logistic regression model predicting depression disorder (DD) in a cohort of females with morbid obesity. (B) Precision–recall curve for propionate, the predictor selected by the best-fit method among all measured variables in a logistic regression model predicting depression disorder (DD) in a cohort of females with morbid obesity. The dashed diagonal line in the ROC plot represents the performance of a random classifier (AUC = 0.5).