Surgically Induced Changes in Gut Microbiome and Hedonic Eating as Related to Weight Loss: Preliminary Findings in Obese Women Undergoing Bariatric Surgery

Abstract

Objective Weight-loss surgery results in significant changes in the anatomy, function and intraluminal environment of the gastrointestinal tract affecting the gut microbiome. While bariatric surgery results in sustained weight loss, decreased appetite and hedonic eating; it is unknown if the surgery-induced alterations in gut microbiota play a role in the observed changes in hedonic eating. We explored the following hypotheses 1) Laparoscopic sleeve gastrectomy results in changes in gut microbial composition ; 2) Alterations in gut microbiota are related to weight loss; 3) Alterations in gut microbiome are associated with changes in appetite and hedonic eating. Methods Eight obese women underwent LSG. Their BMI, body fat mass, food intake, hunger, hedonic eating scores, and stool samples were obtained at baseline and 1-month post-surgery. 16S ribosomal RNA gene sequencing was performed on stool samples. DESeq2 for changes in microbial abundance. Multilevel-sparse partial least squares discriminant analysis was applied to genus level abundance for discriminative microbial signatures. Results LSG resulted in significant reductions in BMI, food intake and hedonic eating. A microbial signature comprised of 5 bacterial genera discriminated between pre and post-surgery status. Several bacterial genera were significantly associated with weight loss (Bilophila (q=3E-05), Faecalibacterium (q=4E-05)), lower appetite (Enterococcus, q=3E-05) and reduced hedonic eating (Akkermansia, q=0.037) after surgery. Conclusions In this preliminary analysis, changes in gut microbial abundance discriminated between pre and postoperative status. Alterations in gut microbiome were significantly associated with weight loss and with reduced hedonic eating after surgery, however a larger sample is needed to confirm these findings.

Systematic review: gastric microbiota in health and disease

“…The Damms‐Machado study, which undertook follow‐up sampling at 3 and 6 months, indicated that a number of butyrate‐producing species including Clostridium , Eubacterium , Faecalibacterium , Dorea and Coprococcus significantly declined post‐operatively with body weight changes positively correlating with Firmicutes numbers. Another study, demonstrated the differential abundance of four genera following surgery, a decrease in Bifidobacteriaceae was seen while an increase in Fusobacterium , Atopobium and Bulleidia were seen. The study by Liu et al did, however, report an increase in alpha‐diversity at 3 months following SG with a specific increase in Bacteroides thetaiotamicron associated with a decrease in BMI and circulating glutamate levels (Figure S1D).…”

“…The majority covered the impact of RYGB (n = 6). [78][79][80][81][82][83] Four studies assessed microbiota changes following SG and LSG 80,[84][85][86] ; one SG study 80 included RYGB as an additional group. The final study 87 looked at the impact of BIB ( Figure S1A).…”

Intestinal-derived FGF15 preserves muscle and bone mass following sleeve gastrectomy

“…Changes in gut microbiota composition are considered a potential contributor to the metabolic benefits of bariatric surgery (Basso et al, 2016;Liou et al, 2013;Sanmiguel et al, 2017). We investigated whether intestinal FGF15 modulates shifts in the microbial communities by VSG.…”

Regenerating motor neurons prime muscle stem cells for myogenesis by enhancing protein synthesis and mitochondrial bioenergetics

“…The lmfit function in R was used to perform a linear regression of each surrogate variable on the two explanatory variables -all surrogate variables were found to have p-value > 0.05 for the regression model fit. Principal component analysis was then performed on the R-log-transform of read counts in the individual biological replicates, using the R-log Transform function within the Deseq2 R package 5 . Surrogate variables were included as covariates in the linear model, resulting in a linear model with gene expression as output and input variables as control vs. injured, and 3 surrogate variables.…”