Unraveling the mystery: a Mendelian randomized exploration of gut microbiota and different types of obesity

Abstract

Background: Numerous studies have demonstrated the influence of gut microbiota on the development of obesity. In this study, we utilized Mendelian randomization (MR) analysis to investigate the gut microbiota characteristics among different types of obese patients, aiming to elucidate the underlying mechanisms and provide novel insights for obesity treatment.

Methods: Two-sample multivariable Mendelian randomization (MR) analysis was employed to assess causal relationships between gut microbiota and various obesity subtypes. Gut microbiota data were obtained from the international consortium MiBioGen, and data on obese individuals were sourced from the Finnish National Biobank FinnGen. Eligible single-nucleotide polymorphisms (SNPs) were selected as instrumental variables. Various analytical methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, MR-RAPS, and Lasso regression, were applied. Sensitivity analyses for quality control included MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses and others.

Results: Mendelian randomization studies revealed distinct gut microbiota profiles among European populations with different obesity subtypes. Following multivariable MR analysis, we found that Ruminococcaceae UCG010 [Odds Ratio (OR): 0.842, 95% confidence interval (CI): 0.766-0.926, Adjusted P value: 0.028] independently reduced the risk of obesity induced by excessive calorie intake, while Butyricimonas [OR: 4.252, 95% CI: 2.177-8.307, Adjusted P value: 0.002] independently increased the risk of medication-induced obesity. For localized adiposity, Pasteurellaceae [OR: 0.213, 95% CI: 0.115-0.395, Adjusted P value: <0.001] acted as a protective factor. In the case of extreme obesity with alveolar hypoventilation, lactobacillus [OR: 0.724, 95% CI: 0.609-0.860, Adjusted P value: 0.035] reduced the risk of its occurrence. Additionally, six gut microbiota may have potential roles in the onset of different types of obesity. Specifically, the Ruminococcus torques group may increase the risk of its occurrence. Desulfovibrio and Catenabacterium may serve as protective factors in the onset of Drug-induced obesity. Oxalobacteraceae, Actinomycetaceae, and Ruminiclostridium 9, on the other hand, could potentially increase the risk of Drug-induced obesity. No evidence of heterogeneity or horizontal pleiotropy among SNPs was found in the above studies (all P values for Q test and MR-Egger intercept > 0.05).

Conclusion: Gut microbiota abundance is causally related to obesity, with distinct gut microbiota profiles observed among different obesity subtypes. Four bacterial species, including Ruminococcaceae UCG010, Butyricimonas, Pasteurellaceae and lactobacillus independently influence the development of various types of obesity. Probiotic and prebiotic supplementation may represent a novel approach in future obesity management.

Keywords: Mendelian randomization; causal relationship; gut microbiota; obesity; stratification.

Figure 1

The study design of the MR study of the associations of gut microbiota on obesity. UV, univariable; MV, multivariable; MR, Mendelian Randomization; GWAS, Genome-Wide Association Study; SNP, Single Nucleotide Polymorphism, used as instrumental variables for exposure and outcome; LD, Linkage Disequilibrium; MR-PRESSO, Mendelian Randomization Pleiotropy RESidual Sum and Outlier; FDR, False Discovery Rate. Parts of the figure were drawn by using pictures from Servier Medical Art.

Figure 2

Significance heatmap of MR analysis. IVW, inverse variance weighted; MR, Mendelian randomization; RAPS, Robust Adjusted Profile Score.

Figure 3

Results and forest plot of the MR analysis. (A) Results and forest plot of the significant UVMR analysis; (B) Results and forest plot of the MVMR analysis; IVW(MRE), inverse variance weighted (multiplicative random effects model); CI, confidence interval; NExp, sample size of exposure dataset; NOut, sample size of outcome dataset; NSNP, number of SNP included in MR analysis; *refer to existence of heterogeneity of SNPs, #refer to existence of pleiotropy between SNPs.