Next Article in Journal
Effect of Vitamin E Supplementation on Chronic Insomnia Disorder in Postmenopausal Women: A Prospective, Double-Blinded Randomized Controlled Trial
Next Article in Special Issue
Megamonas funiformis, Plasma Zonulin, and Sodium Intake Affect C3 Complement Levels in Inactive Systemic Lupus Erythematosus
Previous Article in Journal
Prospective Associations between Cumulative Average Intake of Flavonoids and Hypertension Risk in the CArdioVascular Disease Association Study (CAVAS)
Previous Article in Special Issue
Effect of Resistant Dextrin on Intestinal Gas Homeostasis and Microbiota
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 5
10.3390/nu15051185
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Red Meat Intake, Indole-3-Acetate, and Dorea longicatena Together Affect Insulin Resistance after Gastric Bypass

by
Ana Paula Aguiar Prudêncio
1,
Danielle Cristina Fonseca
1,
Natasha Mendonça Machado
1,
Juliana Tepedino Martins Alves
2,
Priscila Sala
3,
Gabriel R. Fernandes
4,
Raquel Susana Torrinhas
1,* and
Dan Linetzky Waitzberg
1
1
Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo 01246-903, SP, Brazil
2
Hospital Sírio Libanês, Department of Medical Clinical Nutrition, Brasilia 70200-730, DF, Brazil
3
Faculty of Nutrition, Universidade São Camilo, São Paulo 04263-200, SP, Brazil
4
Instituto Rene Rachou, Fiocruz Minas, Belo Horizonte 30190-002, MG, Brazil
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(5), 1185; https://doi.org/10.3390/nu15051185
Submission received: 13 January 2023 / Revised: 19 February 2023 / Accepted: 20 February 2023 / Published: 27 February 2023
(This article belongs to the Special Issue Diet–Host–Microbiota Interaction to Regulate Intestinal Homeostasis)
Browse Figures
Review Reports Versions Notes

Abstract

:
Roux-en-Y Gastric bypass (RYGB) promotes improvement in type 2 diabetes (T2D) shortly after surgery, with metabolic mechanisms yet to be elucidated. This study aimed to investigate the relationship between food intake, tryptophan metabolism, and gut microbiota on the glycemic control of obese T2D women after RYGB surgery. Twenty T2D women who underwent RYGB were evaluated before and three months after surgery. Food intake data were obtained by a seven-day food record and a food frequency questionnaire. Tryptophan metabolites were determined by untargeted metabolomic analysis, and the gut microbiota was determined by 16S rRNA sequencing. The glycemic outcomes were fasting blood glucose, HbA1C, HOMA-IR, and HOMA-beta. Linear regression models were applied to assess the associations between the changes in food intake, tryptophan metabolism, and gut microbiota on glycemic control after RYGB. All variables changed after RYGB (p < 0.05), except for tryptophan intake. Jointly, the variation in red meat intake, plasma indole-3-acetate, and Dorea longicatena was associated with postoperative HOMA-IR {R2 0.80, R2 adj 0.74; p < 0.01}. Red meat intake decreased three months after bariatric surgery while indole-3-acetate and Dorea longicatena increased in the same period. These combined variables were associated with better insulin resistance in T2D women after RYGB.

1. Introduction

Metabolic surgery is a successful treatment for morbid obesity and type 2 diabetes (T2D) [1]. In addition, improvement of T2D after Roux-en-Y Gastric bypass (RYGB) has been noted shortly after surgery, and it is not entirely explained only by weight loss. Many factors have been proposed to elucidate the glycemic improvement of T2D patients following RYGB, including age [2], T2D diagnosis time [2,3], preserved beta pancreatic cell function [4], and preoperative C-peptide levels [5].
Recently, the expansion of metabolomic investigations has raised new evidence linking changes in several metabolites with glycemic enhancement after RYGB, particularly phospholipids, long-chain fatty acids, bile acids, and amino acids [6]. In the field of amino acid research, tryptophan has gained attention, and two studies have demonstrated a relationship between tryptophan metabolites and glucose homeostasis after RYGB [7,8].
Tryptophan, an essential neutral amino acid, is not endogenously synthesized by humans. Dietary intake is necessary, and the main tryptophan food sources include milk and dairy products, eggs, meat, cocoa, and peanuts [9]. In addition to being a serotonin precursor, tryptophan participates in many metabolic pathways and physiological responses. The kynurenine pathway is responsible for approximately 95% of the circulating tryptophan degradation. Metabolites of this pathway are involved in inflammation, and immune response [9], and have been found to be related to diabetes [10]. Kynurenine metabolites can be synthesized by both the host metabolism and gut microbiota. Some bacteria participate in the conversion of tryptophan into kynurenines and derivatives, such as Clostridium clostrioforme and the genus Staphylococcus ssp. [11].
Yet another tryptophan metabolic pathway is the indole pathway, triggered in the host, but mainly by gut microbiota bacteria [12] such as Bifidobacterium longum, Bacteroides fragilis, and Eubacterium halli [13]. Metabolites from the indole pathway have a physiological effect by stimulating enteroendocrine cells to secrete glucagon-like secretory peptide-1 (GLP-1) [13]. GLP-1 has a hypoglycemic action due to its insulinotropic properties and the ability to delay apoptosis of pancreatic beta cells [14]. However, to the best of our knowledge, the relationship between indole derivatives and gut microbiota on glycemic improvement after RYGB has not yet been studied in humans.
The intake of fiber and certain food groups seem to have an impact on the circulating metabolites of tryptophan. Qi et al. (2022), when studying 3938 participants from the HCHS/SOL Cohort, observed positive associations between red meat and refined cereal intake with metabolites of the kynurenine pathway, as well as positive associations between the consumption of dietary fiber and indole derivatives [12].
The impact of food intake on tryptophan and glycemic metabolism is already known, as are the changes in gut microbiota after RYGB [15,16,17,18]. Moreover, tryptophan metabolites produced both by host and gut bacteria appear to be related to the physiopathology of obesity and T2D as well as to the T2D improvement after RYGB [7,8]. However, the effect of changes in food intake, gut microbiota, and tryptophan metabolites on glycemic homeostasis is not yet thoroughly understood. Thus, we aimed to investigate the relationship between food intake, tryptophan metabolism, and gut microbiota on glycemic control in obese T2D women after RYGB surgery. The impact of our findings includes new insight into the knowledge of T2D relief after RYGB and raises possible future therapeutic targets.

2. Materials and Methods

2.1. Study Design and Subjects

This is a single-institution study, approved by the local ethics committee (CaPPesq 4.019.801), which was a part of the SURmetaGIT trial [19] registered with www.ClinicalTrials.gov (NCT01251016; 8 December 2015). Written informed consent was acquired from all participants before the beginning of the study. All protocol interventions were performed following the Declaration of Helsinki guidelines.
Women with obesity-related T2D and candidates for RYGB were recruited from the Surgical Gastroenterology Department of the Hospital das Clínicas of the University of São Paulo, School of Medicine. Data collection was performed between February 2011 and December 2014. Inclusion criteria were as follows: women (18–60 years) with body mass index (BMI) ≥ 35 kg/m2, associated with T2D diagnosis (fasting blood glucose [FBG] ≥ 126 mg/dL and glycated hemoglobin [HbA1C] ≥ 6.5%) and/or use of oral hypoglycemic agent [20]. Patients with recent participation in other interventional study protocols, or with Helicobacter pylori infection, thyroid, or hepatic diseases, under insulin therapy or antibiotic, probiotic, and prebiotic use in the month preceding fecal sample collection were excluded. The RYGB procedure was previously described in the SURMETAGIT protocol [19]. Briefly, open RYGB without silicon rings with a standardized length of biliary-pancreatic limb (50–60 cm) and alimentary limb (100–120 cm) were performed. Food intake surveys, plasma, and fecal samples were collected before and three months after RYGB. Plasma samples were obtained by centrifugation (2800 rpm at 4 °C for 10 min) of blood samples collected after a 12-h fast in ethylenediaminetetraacetic acid (EDTA)-containing tubes (Complete™ mini, EDTA free, Lifescience, Roche Diagnostics Corporation, Indianapolis, IN, USA). These samples were maintained at −80 °C until biochemical and metabolomic analysis. The fecal samples were self-collected by the patients at home, by using a specific specimen collection system (Commode Specimen; Fisher Scientific, Ottawa, ON, Canada). After collection, fecal samples were immediately frozen at −20 °C and transported under controlled temperature to our laboratory, where they were immediately aliquoted (100 mg) into cryogenic vials (without thawing) and stored at −80 °C until gut microbiota evaluations.

2.2. Food Intake

Food intake data were obtained by a seven-day food record (7dFR) and a food frequency questionnaire (FFQ), applied one week before both stool sample collections, as previously described by our group [19]. Briefly, food reported in 7dFR was registered in cooking units (such as tablespoons), guided by illustrations from a manual offered to all patients [21]. The research team converted these units to grams or milliliters after standardization [22]. Energy intake, macronutrients, and total fiber were determined by Virtual Nutri Plus® software, which includes the Brazilian Table of Food Composition (TACO) [23] and the Table of Food Composition: Support to Nutritional Decision [24]. In the present study, we estimated the tryptophan, usual energy, and nutrient intake, as well as the food groups of interest. Tryptophan intake was determined from food sources available in the Brazilian Food Composition Table [23] and the Food Composition Table of the United States Department of Agriculture (USDA) [25]. The Multiple Source Method (MSM) was applied to estimate the usual energy and nutrient intakes through the online platform [26]. All nutrients were adjusted for total energy intake by the residue method [27]. From the FFQ, we determined the daily intake of two food groups: red meat (beef and viscera, i.e., liver, heart, and kidney) and refined cereals (rice, pasta, bread, cakes, salty crackers, and cookies).

2.3. Tryptophan Metabolites

Tryptophan Metabolites were identified in plasma samples by untargeted metabolomic analysis, performed previously by our group at the NIH West Coast Metabolomics Center (WCMC), located at the Genome Center at the University of California, Davis (United States of America) [28]. Seven tryptophan metabolites were captured by mass spectrophotometry using a multiplatform approach combining three analytical platforms: 6530 Accurate-Mass Q-TOF LC/MS e Agilent 1290 Infinity II LC System (Agilent Technologies Ò), a high-performance liquid chromatography (HPLC)—TOF tandem mass spectrometer (MS/MS) method with hydrophilic interaction column (HILIC)—for polar compounds—and the charged hybrid surface column (CSH) for non-polar compounds; and TOF-coupled gas chromatography on the Agilent 6890 GC Pegasus III TOF MS instrument.
The analytical variation and the reproducibility of the profiles were verified during the analyses with standard technic to guarantee the consistency of the results and ensure the instrument itself did not cause large random or systematic deviations from the data obtained during sample acquisition. This was accomplished using a mixture of reference molecules that covered all chemical classes of the metabolites identified in typical analyses (quality control samples).
The raw data obtained were converted using the Analysis Base File Converter software (Reifycs Inc., Tokyo, Japan). Data from metabolites identified by LC-MS were processed by the free MS-DIAL software developed at WCMC (http://prime.psc.riken.jp/Metabolomics_Software/; 13 November 2017). Primary metabolites analyzed by GC-MS spectra were processed based on BinBase data. The results were filtered based on multiple parameters to exclude inconsistent peaks. All BinBase entries were compared to mass spectra from the FiehnLib library with 1200 authentic spectra using retention index information and mass spectra or from library 11 from the National Institute of Standards Technology (NIST).
Data obtained from the plasma samples were reported as the height of quantitative peaks, normalized by the sum of the intensity of all identified metabolites (mTIC), and used for further statistical analyses.
The fold change (FC) was applied to determine the relative changes and to describe its effect size and the direction of metabolite changes. The calculation consisted of the ratio between the postoperative/preoperative mean, and values < 1 were converted and added with a negative sign (−).

2.4. Gut Microbiota (GM)

The GM evaluations were previously developed at MetaGenoPoliS at Jouy-en-Josas, France (http://www.mgps.eu; 5 October 2017) by obtaining fecal DNA and amplifying the V4 region of the 16S rRNA gene, as detailed in the International Human Microbiome Standards (IHMS) SOP06 (http://www.microbiome-standards.org; 11 October 2017) and documented by our group [29]. In the present study, the bioinformatic analysis of the 16S rRNA data was conducted by amplicon sequence variants (ASV) analysis to achieve better resolution for bacteria identification. ASV analysis was carried out at the Bioinformatics Platform in Rene Rachou Institute, Fiocruz Minas (Belo Horizonte, MG, Brazil. Briefly, raw sequence reads of the 16S rRNA gene analysis and ASV calling were performed using the DADA2 [30]. The primers used in the amplification were removed, and sequences with more than two expected errors were discarded. The remaining sequences were used to train an error identification and correction model. The forward and reverse readings, already corrected, were concatenated to form ASVs, remove chimeric sequences, and quantify ASVs. Each ASV had its taxonomic classification assigned by the TAG.ME package [31], using the specific model for the amplicon that corresponds to the V4 region, according to the Silva database [32]. The alpha diversity indexes of the microbial communities (Simpson, Shannon, observed species, Fisher, Ace, and Chao1) were calculated using the Phyloseq package (1.40.0) [33].

2.5. Outcomes

Biomarkers of glycemic control were used as outcomes in statistical regression models. Systemic concentrations of fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and insulin were measured by an enzymatic method (glucose), liquid chromatography (HbA1c), and electrochemiluminescence (insulin), at the Central Laboratory Division of HC-FMUSP, as previously described by our group [19]. Additionally, the Homeostasis Assessment Model (HOMA) was applied to determine the degree of insulin resistance (IR) and the functional capacity of the pancreatic beta cells (Beta) [34].

2.6. Statistical Analysis

Continuous variables are presented as the mean and standard deviation or median and interquartile range, while categorical variables are presented as absolute and relative frequencies. The normality of continuous variables was assessed using the Shapiro–Wilk test.
Differences in the relative abundance of gut microbiota bacteria between the periods studied (preoperative and three months postoperatively) were determined using the Phyloseq package (1.40.0) [33]. Comparisons between each period of the variables of food consumption and metabolites were performed by paired t-test or Wilcoxon test. A significance level of 5% (p < 0.05) was adopted for these analyses.
Associations between the independent variables—food consumption, metabolites, and intestinal microbiota—individually or in groups—and the variables related to glycemic control (outcomes) were assessed by simple and multiple linear regression, respectively. For GI variables, we included only bacteria that vary between periods (p < 0.05) in the regression models. We adopted a significance level of 5% (p < 0.05) to evaluate the glycemic outcomes affected by the independent variables—alone or within its groups (food consumption, metabolites, and intestinal microbiota).
To investigate the effect of combined variables from the different groups (food consumption, metabolites, and intestinal microbiota) on glycemic outcomes we performed multiple regression models. Initially, we performed linear regression models to pre-select independent variables that were associated with glycemic outcomes individually or in groups (p ≤ 0.1) [35]. After this previous selection of independent variables, the olsrr package [36] was used to find the best combination of two or more variables from different groups that could explain the dependent variable in question (FBG, HbA1c, HOMA-IR, and HOMA-Beta). The best subset of predictors was estimated for each dependent variable. The selection of these subsets was based on the F statistic values, the significance of the estimates, adjusted R2, mean square error, Masllow’s Cp, and Akaike’s information criterion. After finding the best models (p < 0.05), they were tested for normality, heteroscedasticity, multicollinearity, and autocorrelation. Finally, based on the best model found to explain the dependent variables in question (FBG, HbA1c, HOMA-IR, and HOMA-Beta), we assumed a significance of p < 0.05 to determine significant effects with the intervention.
The comparison tests were performed with the Statistical Package for Social Science (SPSS) program, version 12.0. For the regression models, specific packages of the R software (version 4.2) were used.

3. Results

3.1. Patient’s Descriptive Data

Twenty women were included in the study. At baseline, participants were 47 ± 6.5 years old, with a BMI of 46.5 ± 5.9 kg/m2, had all glycemic biomarkers compatible with T2D, and used at least one oral hypoglycemic agent. As shown in Table 1, all anthropometric and biochemical data changed after RYGB, except for High-Density Lipoprotein Cholesterol (HDL-c) levels. Biomarkers of glycemic control indicate that RYGB promoted an improvement of T2D. Only two participants maintained the use of oral hypoglycemic agents at three months after surgery.

3.2. Food Intake

Three months after RYGB, all participants presented a reduction in their intake of energy, macronutrients, red meat, and refined cereals. However, probably due to the changes in food choices after surgery when protein food groups such as milk and eggs were preferred over meat, tryptophan intake did not differ between the two recorded periods (Table 2). In addition, 35% of patients reported albumin supplement intake. These food groups and albumin supplements are sources of tryptophan.

3.3. Tryptophan Metabolites

As shown in Table 3, RYGB promoted changes in plasma tryptophan metabolites; N-acetyl-serotonin and indole-3-acetate increased after surgery. Conversely, only anthranilic acid decreased in the same period. These changes indicate a metabolic effect of RYGB on the three major tryptophan pathways (Figure 1).

3.4. Gut Microbiota (GM)

RYGB did not change the GM alpha diversity (Supplementary Figure S1) but affected the GM composition. We observed changes in 27 ASV bacteria taxa between pre- and postoperative time points, in which 3 were reduced and 24 increased. These represent 5 differences in bacteria phyla, and 22 differences in species, as described in Table 4. As shown in Figure 2, among the most prevalent gut bacteria phyla, only Verrucomicrobia and Fusobacteria abundance increased three months after RYGB (vs. preoperative).

3.5. Regression Models

Univariate and multivariate regression models showed associations between the variation (∆ postoperative—preoperative) in food intake, tryptophan metabolites, and gut microbiota with surrogate markers of glycemic control after bariatric surgery (Supplementary Table S1). The variation in tryptophan metabolites, individually or together, did not affect any postoperative glycemic biomarkers (p > 0.05). The variation in Fusobacterium nucleatum (sq381) was directly associated with postoperative glycemia {0.09 (0.02, 0.16); p = 0.05}.
For food intake, a variation in red meat intake was positively associated with postoperative glycemia {0.10 (0.03,0.17); p = 0.03} and HOMA-IR {0.01 (0.005, 0.01); p = 0.002}. Therefore, the greater reduction in red meat intake after RYGB was directly associated with postoperative glycemia and HOMA-IR. In addition, multiple linear regression revealed that the variation in protein {−0.05 (−0.09, −0.02); p = 0.03} and red meat {0.01 (0.01, 0.01); p = 0.0001} intake, individually and together, affected HOMA-IR (R2 0.68, adjusted R2 0.63; p < 0.01). Individually, a variation in protein intake was inversely associated with HOMA-IR, thus, the smaller reduction in protein intake after RYGB, the better improvement of postoperative insulin resistance.
The only significant model that assembled variables from each group (food intake, metabolites, and gut microbiota) on dependent glycemic variables was the HOMA-IR model (Table 5). Together, the reduction in red meat intake {0.01 (0.005, 0.01); p = 0.0003}, an increase in plasma indole-3-acetate {−0.001 (−0.001, −0.0001); p = 0.06}, and Dorea longicatena (sq1408) {0.03 (0.01, 0.06); p = 0.06} were able to explain the improvement of postoperative insulin resistance (HOMA-IR) {R2 0.80, R2 adj 0.74; p < 0.01}.

4. Discussion

Our study showed that RYGB promoted the glycemic improvement of all biomarkers evaluated. At three post-operative months, all patients achieved targets for fasting blood glucose, %HbA1c, and HOMA-IR. We also demonstrated that food intake, gut microbiota, and tryptophan metabolite changes affected glucose homeostasis after RYGB.
Regarding food intake, we observed changes in protein source choices during the postoperative period, marked by a decrease in red meat intake. Reduction in red meat consumption is very common after RYGB as it is reported to be a less tolerated food after the anatomical changes induced by the surgery [37,38,39]. This intolerance may be a consequence of changes in protein digestion caused by the reduction in pepsin synthesis in the gastric pouch, as well as inadequate chewing and increased satiety due to changes in the gut hormones involved in gastric motility and gastric acid secretion [37,38].
Variation in red meat intake was positively associated with postoperative glycemia and HOMA-IR, while protein intake variation was inversely related to postoperative HOMA-IR. Since red meat intake was reduced after RYGB, postoperative glycemia and insulin resistance could be partially explained by a significant decrease in red meat intake. Literature data regarding the association between red meat intake and T2D risk are conflicting. While observational studies have suggested that a higher red meat intake increases the risk of T2D incidence [40,41,42], randomized controlled trials do not confirm associations of red meat intake patterns with the glycemic biomarkers of T2D patients [43]. These divergent results could be attributed to confounding factors that affect glycemic homeostasis and are usually associated with red meat intake, such as alcohol consumption, sedentary lifestyle, and low fiber intake [43]. Nevertheless, red meat compounds seem to affect both beta pancreatic cell function and hepatic insulin extraction by increasing reactive oxygen species and hepatic glucose synthesis, respectively [44].
Decreased protein intake after RYGB was associated with improved postoperative insulin resistance. The anatomic gastrointestinal changes promoted by RYGB increase the availability of partially-digested nutrients to intestinal microbiota, including proteins [45]. When they reach the large intestine, partially digested proteins induce proteolytic bacteria growth, increasing the potential to synthesize pro-inflammatory metabolites, such as hydrogen sulfide (H2S) and trimethylamine-N-oxide (TMAO) [46]. However, a dietary source of protein may influence the intestinal microbiota composition [47]. In this context, reduced red meat intake and a preference for milk, eggs, and albumin supplements seem to decrease some fecal pro-inflammatory bacterial species [48], shifting the gut microbiota composition to more beneficial bacteria [49], which may interfere with glucose homeostasis [50].
Fusobacterium (F.) nucleatum was increased after surgery and its changes affected postoperative glycemia improvement. F. nucleatum is an anaerobic bacterium that engages in diverse interactions with other microorganisms and humans and can be both beneficial and detrimental in nature [51]. To the best of our knowledge, this is the first study to report the association between F. nucleatum and glycemia levels after RYGB. Thus, the mechanisms involved in this finding remain unclear. In our study, increased Akkermansia Muciniphila and reduced Faecalibacterium prausnitzii did not affect T2D improvement after RYGB. This is notable as both bacteria have been associated with better metabolic biomarkers in healthy and T2D individuals [52,53,54,55].
Changes in tryptophan metabolites induced by RYGB included decreased anthranilic acid and an increase in both N-acetyl-serotonin and indole-3-acetate; these alterations might indicate a downstream change in the kynurenine pathway, with a shift towards the serotonin and indole pathways, respectively. This redirection of tryptophan pathways may be due to a low-grade inflammation reduction [56] and gut microbiota changes after RYGB [57]. Tryptophan is converted to kynurenine by pro-inflammatory and stress hormones and activation of the IDO and TDO enzymes, respectively [58]. The new scenario with fewer pro-inflammatory signals may reduce the conversion of tryptophan to kynurenine so more tryptophan is available for the indole and serotonin pathways [59]. Furthermore, indole producer bacteria have been shown to increase after surgery, such as Dorea longicatena, and Akkermansia muciniphila [11].
Moreover, changes in tryptophan metabolites after bariatric surgery have been described. Christensen et al. 2018 [60] showed a reduction in plasma tryptophan, kynurenine, and all kynurenine metabolites, except anthranilic acid, three months after bariatric surgery (sleeve gastrectomy and biliopancreatic diversion with duodenal switch). Favennec et al. 2016 [7] also observed a reduction in plasma tryptophan, kynurenine, and all kynurenine metabolites, and an increase in serotonin one year after sleeve gastrectomy and RYGB. Kwon et al. 2021 [61] reported an increase in indoxyl sulfate but no changes in indole-3-acetate and indole-3-pyruvate three months after sleeve gastrectomy. Yeung et al. 2022 found reduced levels of tryptophan, kynurenic acid, and xanthurenic acid three months after RYGB [8].
In disagreement with these four studies, we did not find an association in the variation of tryptophan metabolites individually on any glycemic biomarkers after bariatric surgery. However, the variation of indole-3-acetate together with red meat intake and Dorea longicatena was able to potentially explain the improvement of postoperative insulin resistance. Dorea longicatena is a producer of indole-3-acetate [11], and both were increased after RYGB. Indole-3-acetate activates aryl hydrocarbon receptors, reducing inflammation and insulin resistance [62]. In addition, indole derivates stimulate insulin secretion through the GLP-1 release [58]. Furthermore, higher plasmatic levels of indole-3-acetic acid have been associated with lower insulin resistance after sleeve gastrectomy [61]. Despite that, to the best of our knowledge, this is the first study to investigate the interaction of food intake, tryptophan metabolism, and gut microbiota variables on glycemic homeostasis, in addition to reporting that alterations of red meat intake, indole-3-acetate, and Dorea longicatena together affects insulin resistance after RYGB.
As a limitation of this present study, the findings do not eliminate the potentiality of other tryptophan metabolites to also affect insulin resistance and glycemic biomarkers after RYGB. In addition, we could only include a small number of participants. Furthermore, some researchers have shown that the experimental absence of gut microbiota could change the concentration of tryptophan in plasma, leading to a reduction in the kynurenine-to-tryptophan ratio [63]. Even if tryptophan metabolism by gut microbiota seems relatively simple at the molecular level, and its metabolic transformation of tryptophan into other metabolites seems undeniable, it is challenging to determine which metabolite each bacterium can produce due to the high diversity and complexity of the microbiome [64]. Thus, we encourage further investigations with a greater panel of indole and kynurenine derivates and a larger number of patients to validate our findings.

5. Conclusions

Early after RYGB, in obese T2D women, there are changes in tryptophan metabolism, food intake, and gut microbiota. Some of these changes can be related to glycemic homeostasis. Together, alterations of red meat intake, indole-3-acetate, and Dorea longicatena seem to improve the surrogate markers associated with insulin resistance at the three-month post-operatory period.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15051185/s1, Supplementary Figure S1: Alpha diversity indices before and three months after RYGB. Supplementary Table S1: Regression models for effects of variation (∆ postoperative—preoperative) of food intake, tryptophan metabolites, and gut microbiota (independent variables) on postoperative Glycemia, HbA1c, HOMA-IR, and HOMA-Beta (dependent variables).

Author Contributions

Conceptualization, A.P.A.P.; methodology, A.P.A.P., N.M.M. and P.S.; validation, D.C.F. and N.M.M.; formal analysis, A.P.A.P. and G.R.F.; investigation, R.S.T. and D.L.W.; resources, D.L.W. and G.R.F.; writing—original draft preparation, A.P.A.P., D.C.F. and J.T.M.A.; writing—review and editing, N.M.M., R.S.T. and D.L.W.; visualization, A.P.A.P. and D.C.F.; supervision, D.L.W.; project administration, D.L.W.; funding acquisition, D.L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by FAPESP 2011/09612-3 and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, approved by the ethics committee of Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil (protocol code 4.019.801 and 11 May 2020) and was part of the original protocol registered with www.ClinicalTrials.gov (NCT01251016; 8 December 2015).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Nucleotide sequence data used for this study are deposited in the Sequence Read Archive (SRA) of the NCBI under the accession number SRP113514.

Acknowledgments

The authors thank Karina Al Assal and MetaGenoPoliS at Jouy-en-Josas (France) for performing Gut Microbiota DNA extraction and sample preparation. Carla Taddei for performing the 16SrRNA sequencing.

Conflicts of Interest

Dan Linetzky Waitzberg is the scientific director of Bioma4me, São Paulo, Brazil. Juliana Tepedino Martins Alves was contemplated with LAM grant Fresenius kABI in Parenteral Nutrition. The other authors declare no conflict of interest.

References

  1. Nguyen, N.T.; Varela, J.E. Bariatric Surgery for Obesity and Metabolic Disorders: State of the Art. Nat. Rev. Gastroenterol. Hepatol. 2017, 14, 160–169. [Google Scholar] [CrossRef] [PubMed]
  2. Chen, X.; Kong, X. Diabetes Remission and Relapse after Metabolic Surgery. J. Diabetes Investig. 2018, 9, 1237–1238. [Google Scholar] [CrossRef] [PubMed]
  3. Vaz, M.; Pereira, S.S.; Monteiro, M.P. Metabolomic Signatures after Bariatric Surgery—A Systematic Review. Rev. Endocr. Metab. Disord. 2022, 23, 503–519. [Google Scholar] [CrossRef]
  4. Souteiro, P.; Belo, S.; Neves, J.S.; Magalhães, D.; Silva, R.B.; Oliveira, S.C.; Costa, M.M.; Saavedra, A.; Oliveira, J.; Cunha, F.; et al. Preoperative Beta Cell Function Is Predictive of Diabetes Remission After Bariatric Surgery. Obes. Surg. 2017, 27, 288–294. [Google Scholar] [CrossRef] [PubMed]
  5. Yan, W.; Bai, R.; Yan, M.; Song, M. Preoperative Fasting Plasma C-Peptide Levels as Predictors of Remission of Type 2 Diabetes Mellitus after Bariatric Surgery: A Systematic Review and Meta-Analysis. J. Investig. Surg. 2017, 30, 383–393. [Google Scholar] [CrossRef]
  6. Sarosiek, K.; Pappan, K.L.; Gandhi, A.V.; Saxena, S.; Kang, C.Y.; McMahon, H.; Chipitsyna, G.I.; Tichansky, D.S.; Arafat, H.A. Conserved Metabolic Changes in Nondiabetic and Type 2 Diabetic Bariatric Surgery Patients: Global Metabolomic Pilot Study. J. Diabetes Res. 2016, 2016, 3467403. [Google Scholar] [CrossRef] [Green Version]
  7. Favennec, M.; Hennart, B.; Verbanck, M.; Pigeyre, M.; Caiazzo, R.; Raverdy, V.; Verkindt, H.; Leloire, A.; Guillemin, G.J.; Yengo, L.; et al. Post-Bariatric Surgery Changes in Quinolinic and Xanthurenic Acid Concentrations Are Associated with Glucose Homeostasis. PLoS ONE 2016, 11, e0158051. [Google Scholar] [CrossRef] [Green Version]
  8. Yeung, K.T.D.; Penney, N.; Whiley, L.; Ashrafian, H.; Lewis, M.R.; Purkayastha, S.; Darzi, A.; Holmes, E. The Impact of Bariatric Surgery on Serum Tryptophan–Kynurenine Pathway Metabolites. Sci. Rep. 2022, 12, 294. [Google Scholar] [CrossRef]
  9. Palego, L.; Betti, L.; Rossi, A.; Giannaccini, G. Tryptophan Biochemistry: Structural, Nutritional, Metabolic, and Medical Aspects in Humans. J. Amino Acids 2016, 2016, 8952520. [Google Scholar] [CrossRef] [Green Version]
  10. Liu, J.-J.; Movassat, J.; Portha, B. Emerging Role for Kynurenines in Metabolic Pathologies. Curr. Opin. Clin. Nutr. Metab. Care 2019, 22, 82–90. [Google Scholar] [CrossRef]
  11. Lu, Y.; Chong, J.; Shen, S.; Chammas, J.-B.; Chalifour, L.; Xia, J. TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome. Metabolites 2021, 12, 10. [Google Scholar] [CrossRef]
  12. Qi, Q.; Li, J.; Yu, B.; Moon, J.-Y.; Chai, J.C.; Merino, J.; Hu, J.; Ruiz-Canela, M.; Rebholz, C.; Wang, Z.; et al. Host and Gut Microbial Tryptophan Metabolism and Type 2 Diabetes: An Integrative Analysis of Host Genetics, Diet, Gut Microbiome and Circulating Metabolites in Cohort Studies. Gut 2022, 71, 1095–1105. [Google Scholar] [CrossRef] [PubMed]
  13. Roager, H.M.; Licht, T.R. Microbial Tryptophan Catabolites in Health and Disease. Nat. Commun. 2018, 9, 3294. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Holz, G.G.; Chepurny, O.G. Glucagon-like Peptide-1 Synthetic Analogs: New Therapeutic Agents for Use in the Treatment of Diabetes Mellitus. Curr. Med. Chem. 2003, 10, 2471–2483. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Zhang, H.; DiBaise, J.K.; Zuccolo, A.; Kudrna, D.; Braidotti, M.; Yu, Y.; Parameswaran, P.; Crowell, M.D.; Wing, R.; Rittmann, B.E.; et al. Human Gut Microbiota in Obesity and after Gastric Bypass. Proc. Natl. Acad. Sci. USA 2009, 106, 2365–2370. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Shen, N.; Caixàs, A.; Ahlers, M.; Patel, K.; Gao, Z.; Dutia, R.; Blaser, M.J.; Clemente, J.C.; Laferrère, B. Longitudinal Changes of Microbiome Composition and Microbial Metabolomics after Surgical Weight Loss in Individuals with Obesity. Surg. Obes. Relat. Dis. 2019, 15, 1367–1373. [Google Scholar] [CrossRef]
  17. Tremaroli, V.; Karlsson, F.; Werling, M.; Ståhlman, M.; Kovatcheva-Datchary, P.; Olbers, T.; Fändriks, L.; le Roux, C.W.; Nielsen, J.; Bäckhed, F. Roux-En-Y Gastric Bypass and Vertical Banded Gastroplasty Induce Long-Term Changes on the Human Gut Microbiome Contributing to Fat Mass Regulation. Cell Metab. 2015, 22, 228–238. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Furet, J.-P.; Kong, L.-C.; Tap, J.; Poitou, C.; Basdevant, A.; Bouillot, J.-L.; Mariat, D.; Corthier, G.; Doré, J.; Henegar, C.; et al. Differential Adaptation of Human Gut Microbiota to Bariatric Surgery-Induced Weight Loss: Links with Metabolic and Low-Grade Inflammation Markers. Diabetes 2010, 59, 3049–3057. [Google Scholar] [CrossRef] [Green Version]
  19. Sala, P.; Belarmino, G.; Machado, N.M.; Cardinelli, C.S.; Al Assal, K.; Silva, M.M.; Fonseca, D.C.; Ishida, R.K.; Santo, M.A.; de Moura, E.G.H.; et al. The SURMetaGIT Study: Design and Rationale for a Prospective Pan-Omics Examination of the Gastrointestinal Response to Roux-En-Y Gastric Bypass Surgery. J. Int. Med. Res. 2016, 44, 1359–1375. [Google Scholar] [CrossRef] [Green Version]
  20. American Diabetes Association 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care 2018, 41, S13–S27. [Google Scholar] [CrossRef] [Green Version]
  21. Monteiro, J.P.; Chiarello, P. Consumo Alimentar: Visualizando Porções, 1st ed.; Guanabara Koogan: Rio de Janeiro, Brazil, 2007; pp. 1–92. [Google Scholar]
  22. Pinheiro, A.B. Tabela para Avaliação de Consumo Alimentar Em Medidas Caseiras, 5th ed.; Atheneu: Sao Paulo, Brazil, 2004; pp. 1–131. [Google Scholar]
  23. NEPA UNICAMP. Tabela Brasileira de Composição de Alimentos TACO, 4th ed.; NEPA UNICAMP: Campinas, Brazil, 2011; pp. 1–161. [Google Scholar]
  24. Philippi, S.T. Table of Food Composition: Nutritional Support for Decision, 2nd ed.; Coronário: Sao Paulo, Brazil, 2002; 135p. [Google Scholar]
  25. U.S Department of Agriculture. USDA Database for the Tryptophan Content of Common Foods. Available online: https://fdc.nal.usda.gov/fdc-app.html#/?component=1210 (accessed on 18 November 2019).
  26. DifE Muliple Source Method (MSM). Available online: https://msm.dife.de/ (accessed on 6 February 2020).
  27. Willett, W.C.; Howe, G.R.; Kushi, L.H. Adjustment for Total Energy Intake in Epidemiologic Studies. Am. J. Clin. Nutr. 1997, 65, 1220S–1228S, discussion 1229S–1231S. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Machado, N.M. Impact of Roux-em-Y Gastric Bypass on Metabolomic Profile of Obese Women with Type 2 Diabetes. Ph.D. Thesis, Doctoral Research Degree—Gastroenterology Science, Universidade de São Paulo, São Paulo, Brazil, 2019. [Google Scholar]
  29. Al Assal, K.; Prifti, E.; Belda, E.; Sala, P.; Clément, K.; Dao, M.-C.; Doré, J.; Levenez, F.; Taddei, C.R.; Fonseca, D.C.; et al. Gut Microbiota Profile of Obese Diabetic Women Submitted to Roux-En-Y Gastric Bypass and Its Association with Food Intake and Postoperative Diabetes Remission. Nutrients 2020, 12, 278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-Resolution Sample Inference from Illumina Amplicon Data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Pires, D.E.V.; Oliveira, F.S.; Correa, F.B.; Morais, D.K.; Fernandes, G.R. TAG.ME: Taxonomic Assignment of Genetic Markers for Ecology. BioRxiv 2018, 263293. [Google Scholar] [CrossRef] [Green Version]
  32. Beccati, A.; Gerken, J.; Quast, C.; Yilmaz, P.; Glöckner, F.O. SILVA Tree Viewer: Interactive Web Browsing of the SILVA Phylogenetic Guide Trees. BMC Bioinform. 2017, 18, 433. [Google Scholar] [CrossRef] [Green Version]
  33. McMurdie, P.J.; Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 2013, 8, e61217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis Model Assessment: Insulin Resistance and Beta-Cell Function from Fasting Plasma Glucose and Insulin Concentrations in Man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Heinze, G.; Wallisch, C.; Dunkler, D. Variable Selection—A Review and Recommendations for the Practicing Statistician. Biom. J. Biom. Z. 2018, 60, 431–449. [Google Scholar] [CrossRef] [Green Version]
  36. Hebbali, A. Tools for Building OLS Regression Models. 2022. Available online: https://cran.r-project.org/web/packages/olsrr/olsrr.pdf (accessed on 12 September 2022).
  37. Nicoletti, C.F.; de Oliveira, B.A.P.; Barbin, R.; Marchini, J.S.; Salgado Junior, W.; Nonino, C.B. Red Meat Intolerance in Patients Submitted to Gastric Bypass: A 4-Year Follow-up Study. Surg. Obes. Relat. Dis. 2015, 11, 842–846. [Google Scholar] [CrossRef]
  38. Boerlage, T.C.C.; van de Laar, A.W.J.M.; Westerlaken, S.; Gerdes, V.E.A.; Brandjes, D.P.M. Gastrointestinal Symptoms and Food Intolerance 2 Years after Laparoscopic Roux-en-Y Gastric Bypass for Morbid Obesity. Br. J. Surg. 2017, 104, 393–400. [Google Scholar] [CrossRef]
  39. de A Godoy, C.M.; Aprígio, L.C.S.; de Godoy, E.P.; Furtado, M.C.; Coelho, D.; de Souza, L.B.R.; de Oliveira, A.M.G. Food Tolerance and Eating Behavior After Roux-En-Y Gastric Bypass Surgery. Obes. Surg. 2018, 28, 1540–1545. [Google Scholar] [CrossRef] [PubMed]
  40. Pan, A.; Sun, Q.; Bernstein, A.M.; Manson, J.E.; Willett, W.C.; Hu, F.B. Changes in Red Meat Consumption and Subsequent Risk of Type 2 Diabetes Mellitus: Three Cohorts of US Men and Women. JAMA Intern. Med. 2013, 173, 1328–1335. [Google Scholar] [CrossRef] [PubMed]
  41. Baleato, C.L.; Ferguson, J.J.A.; Oldmeadow, C.; Mishra, G.D.; Garg, M.L. Plant-Based Dietary Patterns versus Meat Consumption and Prevalence of Impaired Glucose Intolerance and Diabetes Mellitus: A Cross-Sectional Study in Australian Women. Nutrients 2022, 14, 4152. [Google Scholar] [CrossRef] [PubMed]
  42. Würtz, A.M.L.; Jakobsen, M.U.; Bertoia, M.L.; Hou, T.; Schmidt, E.B.; Willett, W.C.; Overvad, K.; Sun, Q.; Manson, J.E.; Hu, F.B.; et al. Replacing the Consumption of Red Meat with Other Major Dietary Protein Sources and Risk of Type 2 Diabetes Mellitus: A Prospective Cohort Study. Am. J. Clin. Nutr. 2021, 113, 612–621. [Google Scholar] [CrossRef] [PubMed]
  43. Sanders, L.M.; Wilcox, M.L.; Maki, K.C. Red Meat Consumption and Risk Factors for Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Eur. J. Clin. Nutr. 2023, 77, 156–165. [Google Scholar] [CrossRef] [PubMed]
  44. Kim, Y.; Keogh, J.; Clifton, P. A Review of Potential Metabolic Etiologies of the Observed Association between Red Meat Consumption and Development of Type 2 Diabetes Mellitus. Metabolism. 2015, 64, 768–779. [Google Scholar] [CrossRef]
  45. Odstrcil, E.A.; Martinez, J.G.; Santa Ana, C.A.; Xue, B.; Schneider, R.E.; Steffer, K.J.; Porter, J.L.; Asplin, J.; Kuhn, J.A.; Fordtran, J.S. The Contribution of Malabsorption to the Reduction in Net Energy Absorption after Long-Limb Roux-En-Y Gastric Bypass. Am. J. Clin. Nutr. 2010, 92, 704–713. [Google Scholar] [CrossRef] [Green Version]
  46. Van Hecke, T.; Vossen, E.; Goethals, S.; Boon, N.; De Vrieze, J.; De Smet, S. In Vitro and in Vivo Digestion of Red Cured Cooked Meat: Oxidation, Intestinal Microbiota and Fecal Metabolites. Food Res. Int. Ott. Ont 2021, 142, 110203. [Google Scholar] [CrossRef]
  47. Bartlett, A.; Kleiner, M. Dietary Protein and the Intestinal Microbiota: An Understudied Relationship. iScience 2022, 25, 105313. [Google Scholar] [CrossRef]
  48. Wu, S.; Bhat, Z.F.; Gounder, R.S.; Mohamed Ahmed, I.A.; Al-Juhaimi, F.Y.; Ding, Y.; Bekhit, A.E.-D.A. Effect of Dietary Protein and Processing on Gut Microbiota-A Systematic Review. Nutrients 2022, 14, 453. [Google Scholar] [CrossRef]
  49. Yin, Y.; Cai, J.; Zhou, L.; Xing, L.; Zhang, W. Dietary Oxidized Beef Protein Alters Gut Microbiota and Induces Colonic Inflammatory Damage in C57BL/6 Mice. Front. Nutr. 2022, 9, 980204. [Google Scholar] [CrossRef] [PubMed]
  50. Zhang, Y.; Hu, J.; Tan, H.; Zhong, Y.; Nie, S. Akkermansia Muciniphila, an Important Link between Dietary Fiber and Host Health. Curr. Opin. Food Sci. 2022, 47, 100905. [Google Scholar] [CrossRef]
  51. Brennan, C.A.; Garrett, W.S. Fusobacterium Nucleatum—Symbiont, Opportunist and Oncobacterium. Nat. Rev. Microbiol. 2019, 17, 156–166. [Google Scholar] [CrossRef]
  52. Xu, Y.; Wang, N.; Tan, H.-Y.; Li, S.; Zhang, C.; Feng, Y. Function of Akkermansia Muciniphila in Obesity: Interactions With Lipid Metabolism, Immune Response and Gut Systems. Front. Microbiol. 2020, 11, 219. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Dao, M.C.; Everard, A.; Aron-Wisnewsky, J.; Sokolovska, N.; Prifti, E.; Verger, E.O.; Kayser, B.D.; Levenez, F.; Chilloux, J.; Hoyles, L.; et al. Akkermansia Muciniphila and Improved Metabolic Health during a Dietary Intervention in Obesity: Relationship with Gut Microbiome Richness and Ecology. Gut 2016, 65, 426–436. [Google Scholar] [CrossRef] [Green Version]
  54. Le Chatelier, E.; Nielsen, T.; Qin, J.; Prifti, E.; Hildebrand, F.; Falony, G.; Almeida, M.; Arumugam, M.; Batto, J.-M.; Kennedy, S.; et al. Richness of Human Gut Microbiome Correlates with Metabolic Markers. Nature 2013, 500, 541–546. [Google Scholar] [CrossRef]
  55. Xu, J.; Liang, R.; Zhang, W.; Tian, K.; Li, J.; Chen, X.; Yu, T.; Chen, Q. Faecalibacterium Prausnitzii-Derived Microbial Anti-Inflammatory Molecule Regulates Intestinal Integrity in Diabetes Mellitus Mice via Modulating Tight Junction Protein Expression. J. Diabetes 2020, 12, 224–236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Biobaku, F.; Ghanim, H.; Monte, S.V.; Caruana, J.A.; Dandona, P. Bariatric Surgery: Remission of Inflammation, Cardiometabolic Benefits, and Common Adverse Effects. J. Endocr. Soc. 2020, 4, bvaa049. [Google Scholar] [CrossRef]
  57. Debédat, J.; Clément, K.; Aron-Wisnewsky, J. Gut Microbiota Dysbiosis in Human Obesity: Impact of Bariatric Surgery. Curr. Obes. Rep. 2019, 8, 229–242. [Google Scholar] [CrossRef]
  58. Agus, A.; Planchais, J.; Sokol, H. Gut Microbiota Regulation of Tryptophan Metabolism in Health and Disease. Cell Host Microbe 2018, 23, 716–724. [Google Scholar] [CrossRef] [Green Version]
  59. Kennedy, P.J.; Cryan, J.F.; Dinan, T.G.; Clarke, G. Kynurenine Pathway Metabolism and the Microbiota-Gut-Brain Axis. Neuropharmacology 2017, 112, 399–412. [Google Scholar] [CrossRef] [PubMed]
  60. Christensen, M.H.E.; Fadnes, D.J.; Røst, T.H.; Pedersen, E.R.; Andersen, J.R.; Våge, V.; Ulvik, A.; Midttun, Ø.; Ueland, P.M.; Nygård, O.K.; et al. Inflammatory Markers, the Tryptophan-Kynurenine Pathway, and Vitamin B Status after Bariatric Surgery. PLoS ONE 2018, 13, e0192169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Kwon, Y.; Jang, M.; Lee, Y.; Ha, J.; Park, S. Metabolomic Analysis of the Improvements in Insulin Secretion and Resistance After Sleeve Gastrectomy: Implications of the Novel Biomarkers. Obes. Surg. 2021, 31, 43–52. [Google Scholar] [CrossRef] [PubMed]
  62. Dong, F.; Perdew, G.H. The Aryl Hydrocarbon Receptor as a Mediator of Host-Microbiota Interplay. Gut Microbes 2020, 12, 1859812. [Google Scholar] [CrossRef]
  63. Bosi, A.; Banfi, D.; Bistoletti, M.; Giaroni, C.; Baj, A. Tryptophan Metabolites Along the Microbiota-Gut-Brain Axis: An Interkingdom Communication System Influencing the Gut in Health and Disease. Int. J. Tryptophan Res. 2020, 13, 1178646920928984. [Google Scholar] [CrossRef]
  64. Dehhaghi, M.; Kazemi Shariat Panahi, H.; Guillemin, G.J. Microorganisms, Tryptophan Metabolism, and Kynurenine Pathway: A Complex Interconnected Loop Influencing Human Health Status. Int. J. Tryptophan Res. 2019, 12, 1178646919852996. [Google Scholar] [CrossRef] [Green Version]
Nutrients 15 01185 g001 550
Figure 1. Schematic representation of the identified metabolites separated by the three major tryptophan pathways. The metabolites changed three months after RYGB are highlighted in bold. Statistical significance p < 0.05.
Figure 1. Schematic representation of the identified metabolites separated by the three major tryptophan pathways. The metabolites changed three months after RYGB are highlighted in bold. Statistical significance p < 0.05.
Nutrients 15 01185 g001
Nutrients 15 01185 g002 550
Figure 2. Relative abundance (%) of changes in gut bacteria phyla before and three months after RYGB. Statistical significance p < 0.05.
Figure 2. Relative abundance (%) of changes in gut bacteria phyla before and three months after RYGB. Statistical significance p < 0.05.
Nutrients 15 01185 g002
Table
Table 1. Characterization of the participants according to clinical and biochemical variables before and three months after RYGB.
Table 1. Characterization of the participants according to clinical and biochemical variables before and three months after RYGB.
VariablesPreoperativePostoperative (3 Months)p-Value 1
Age, years 46.6 ± 6.5NA 2
Albumin supplement, n (%) 0 (0)7 (35)
Oral hypoglycemic
Metformin, n (%)5 (25)1 (5)
Glibenclamide, n (%)1 (5)0 (0)
Acarbose, n, (%)0 (0)0 (0)
Glicazide, n (%)0 (0)0 (0)
Multimedication, n (%) 14 (70)1 (5)
Antihyperlipidemic agents, n (%) 39 (45)2 (10)
Anthropometric data
Weight, kg114.4 ± 16.493.6 ± 12.90.000
BMI 4, kg/m246.5 ± 5.938.0 ± 4.60.000
EWL 5, %NA233.7 ± 5.5
Biochemical data
FBG 6, mg/dL219.6 ± 77.2100.6 ± 19.90.000
HbA1c, %9.3 ± 1.76.1 ± 0.40.000
Insulin, µU/mL15.7 (6.8)7.8 (4.9)0.001
HOMA 7-IR7.9 (5.7)1.9 (1.2)0.000
HOMA 7 Beta47.1 (53.3)90.8 (72.2)0.019
Triglycerides, mg/dL136 (64)103 (25)0.012
HDL-c 8, mg/dL45.3 ± 10.442.9 ± 10.20.254
1 paired t-test or Wilcoxon test, statistical significance p < 0.05. 2 NA = not applicable. 3 Statins and fibrates. 4 BMI = Body Mass Index. 5 EWL = excess weight lost. 6 FBG = fasting blood glucose. 7 HOMA = Homeostatic model assessment. 8 HDL-c = High-Density Lipoprotein Cholesterol. Data are shown as the mean ± standard deviation or median (interquartile range).
Table
Table 2. Food intake variables before and three months after RYGB.
Table 2. Food intake variables before and three months after RYGB.
VariablesPreoperativePostoperative (3 Months)p-Value 1
Energy, kcal1.696.9 ± 375.4973.2 ± 211.60.000
Protein, g68.7 ± 11.947.9 ± 13.90.006
Protein, %16.7 ± 2.322.2 ± 5.60.000
Carbohydrates, g209.8 ± 42.4107.7 ± 25.50.000
Carbohydrates, %50.9 ± 3.744.8 ± 5.50.000
Lipids, g62.1 ± 15.537.7 ± 7.70.000
Lipids, %33.6 ± 3.535.4 ± 3.50.083
Fiber, g14.8 ± 5.39.1 ± 3.40.000
Tryptophan, mg232.1 (66.1)230.5 (170.0)0.299
Tryptophan, mg/kg2.0 (1.0)2.4 (2.3)0.027
Red meat, g51.4 (103.0)28.9 (26.8)0.015
Refined cereals, g361.8 (341.2)59.2 (60.4)0.000
1 paired t-test or Wilcoxon test, statistical significance p < 0.05. Data are shown as the mean ± standard deviation or median (interquartile range).
Table
Table 3. Changes in tryptophan metabolites three months after RYGB.
Table 3. Changes in tryptophan metabolites three months after RYGB.
Plasma MetabolitesFold Changep-Value 1
Tryptophan−1.620.112
N-acetyl-serotonin1.320.008
Indole-3-acetate1.770.016
Glutamic acid−1.070.372
L-alanine1.060.528
α-ketoglutarate acid1.140.157
Anthranilic acid−1.870.004
1 Wilcoxon test, statistical significance p < 0.05.
Table
Table 4. Relative abundance (%) of gut bacteria species before and three months after RYGB.
Table 4. Relative abundance (%) of gut bacteria species before and three months after RYGB.
Bacterial TaxonomyPreoperativePostoperativep-adj 1
sqPhylumClassOrderFamilyGenusSpecies(3 Months)
sq18FirmicutesClostridiaClostridialesVeillonellaceaeVeillonellaVeillonella parvula0.103 ± 0.1371.827 ± 4.1870.0002
sq28FirmicutesBacilliLactobacillalesStreptococcaceaeStreptococcusStreptococcus salivarius0.240 ± 0.5621.385 ± 2.2160.0037
sq58FirmicutesClostridiaClostridialesLachnospiraceaeLachnoclostridiumClostridium clostridioforme0.115 ± 0.2420.940 ± 3.2780.0421
sq96FirmicutesClostridiaClostridialesLachnospiraceaeBlautiaBlautia luti0.538 ± 0.6690.141 ± 0.2220.0020
sq80FirmicutesNegativicutesSelenomonadalesVeillonellaceaeVeillonellaVeillonella parvula0.143 ± 0.5680.482 ± 0.8870.0069
sq93FirmicutesBacilliLactobacillalesStreptococcaceaeStreptococcusStreptococcus parasanguinis0.062 ± 0.1850.456 ± 0.6060.0045
sq315FirmicutesNegativicutesSelenomonadalesVeillonellaceaeVeillonellaVeillonella parvula0.000 ± 0.0000.079 ± 0.1800.0225
sq311FirmicutesBacilliBacillalesFamily XIGemellaGemella haemolysans0.006 ± 0.0100.069 ± 0.0970.0064
sq338FirmicutesClostridiaClostridialesClostridiaceae 1SarcinaClostridium tarantellae0.005 ± 0.0200.064 ± 0.0990.0167
sq380FirmicutesClostridiaClostridialesLachnospiraceaeLachnoclostridiumClostridium lavalense0.011 ± 0.0190.047 ± 0.0920.0033
sq421FirmicutesBacilliLactobacillalesCarnobacteriaceaeGranulicatellaGranulicatella adiacens0.003 ± 0.0090.039 ± 0.0560.0253
sq768FirmicutesClostridiaClostridialesLachnospiraceaeEisenbergiellaEisenbergiella tayi0.002 ± 0.0070.018 ± 0.0380.0209
sq649FirmicutesClostridiaClostridialesRuminococcaceaeFaecalibacteriumFaecalibacterium prausnitzii0.013 ± 0.0210.003 ± 0.0060.0440
sq691FirmicutesClostridiaClostridialesLachnospiraceaeLachnoclostridiumClostridium clostridioforme0.003 ± 0.0080.011 ± 0.0190.0445
sq1127FirmicutesBacilliLactobacillalesEnterococcaceaeEnterococcusEnterococcus faecalis0.001 ± 0.0040.004 ± 0.0080.0120
sq1542FirmicutesClostridiaClostridialesLachnospiraceaeOribacteriumOribacterium parvum0.000 ± 0.0000.004 ± 0.0060.0223
sq1408FirmicutesClostridiaClostridialesLachnospiraceaeRuminococcus torques groupDorea longicatena0.000 ± 0.0000.003 ± 0.0050.0360
sq1316FirmicutesNegativicutesSelenomonadalesVeillonellaceaeDialisterDialister invisus0.000 ± 0.0010.002 ± 0.0030.0440
sq1698FirmicutesClostridiaClostridialesFamily XIIIMogibacteriumMogibacterium vescum0.000 ± 0.0000.002 ± 0.0030.0225
sq2371FirmicutesClostridiaClostridialesPeptostreptococcaceaeClostridioidesClostridioides difficile0.001 ± 0.0020.000 ± 0.0000.0213
sq368FusobacteriaFusobacteriiaFusobacterialesFusobacteriaceaeFusobacteriumFusobacterium periodonticum0.002 ± 0.0070.055 ± 0.0990.0016
sq381FusobacteriaFusobacteriiaFusobacterialesFusobacteriaceaeFusobacteriumFusobacterium nucleatum0.000 ± 0.0000.039 ± 0.0790.0025
sq612FusobacteriaFusobacteriiaFusobacterialesFusobacteriaceaeFusobacteriumFusobacterium nucleatum0.000 ± 0.0000.019 ± 0.0320.0092
sq1129FusobacteriaFusobacteriiaFusobacterialesFusobacteriaceaeFusobacteriumFusobacterium nucleatum0.000 ± 0.0000.004 ± 0.0090.0143
sq3ProteobacteriaGammaproteobacteriaEnterobacterialesEnterobacteriaceaeEscherichia-ShigellaEscherichia coli1.255 ± 2.5226.397 ± 6.4100.0003
sq647ProteobacteriaGammaproteobacteriaBetaproteobacterialesNeisseriaceaeNeisseriaNeisseria flavescens0.000 ± 0.0000.008 ± 0.0140.0141
sq15VerrucomicrobiaVerrucomicrobiaeVerrucomicrobialesAkkermansiaceaeAkkermansiaAkkermansia muciniphila0.010 ± 0.0182.116 ± 4.7610.0280
1 p adjusted: Wilcoxon test, statistical significance p < 0.05. Data are shown as the mean ± standard deviation.
Table
Table 5. Multiple regression model with the effect of variation (∆ postoperative—preoperative) of food intake, tryptophan metabolites, and gut microbiota (independent variables) on postoperative HOMA-IR (dependent variable).
Table 5. Multiple regression model with the effect of variation (∆ postoperative—preoperative) of food intake, tryptophan metabolites, and gut microbiota (independent variables) on postoperative HOMA-IR (dependent variable).
VariablesHOMA-IR 1
Food intake
Red meat (g)0.01 (0.005, 0.01)
p = 0.0003
Metabolites
Indole-3-acetate−0.001 (−0.001, −0.0001)
p = 0.06
Gut Microbiota
Dorea longicatena (sq1408)0.03 (0.01, 0.06)
p = 0.06
1 Multivariate regression model: HOMA-IR R2 0.80; R2 adj 0.74 (p < 0.01). Statistical significance p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Prudêncio, A.P.A.; Fonseca, D.C.; Machado, N.M.; Alves, J.T.M.; Sala, P.; Fernandes, G.R.; Torrinhas, R.S.; Waitzberg, D.L. Red Meat Intake, Indole-3-Acetate, and Dorea longicatena Together Affect Insulin Resistance after Gastric Bypass. Nutrients 2023, 15, 1185. https://doi.org/10.3390/nu15051185

AMA Style

Prudêncio APA, Fonseca DC, Machado NM, Alves JTM, Sala P, Fernandes GR, Torrinhas RS, Waitzberg DL. Red Meat Intake, Indole-3-Acetate, and Dorea longicatena Together Affect Insulin Resistance after Gastric Bypass. Nutrients. 2023; 15(5):1185. https://doi.org/10.3390/nu15051185

Chicago/Turabian Style

Prudêncio, Ana Paula Aguiar, Danielle Cristina Fonseca, Natasha Mendonça Machado, Juliana Tepedino Martins Alves, Priscila Sala, Gabriel R. Fernandes, Raquel Susana Torrinhas, and Dan Linetzky Waitzberg. 2023. "Red Meat Intake, Indole-3-Acetate, and Dorea longicatena Together Affect Insulin Resistance after Gastric Bypass" Nutrients 15, no. 5: 1185. https://doi.org/10.3390/nu15051185

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop