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. 2026 Aug 19;13:1912097. doi: 10.3389/fnut.2026.1912097

Daily fiber supplementation after bariatric surgery demonstrates tolerability and influence on the fecal gut microbiome

Brandy Moser 1,*, Solange M Saxby 2, Stephanie Lebby 3, Jennifer D Letendre 4, Sarah Lange 4, Marissa A Mendez 4, Thadeus L Trus 4, Elizabeth Honigsberg 3, Sarah E Billmeier 4, Diane Gilbert-Diamond 5, Maria Carlota Dao 1, Jennifer L Meijer 6
PMCID: PMC13536892  PMID: 42689141

Abstract

Introduction

This study assessed the feasibility and tolerance of a 30-day resistant potato starch (RPS) supplement in patients who recently underwent bariatric surgery and its effect on microbial production of fecal short-chain fatty acids (SCFA).

Methods

This clinical trial (ClinicalTrials.gov, identifier: NCT05653648), conducted at Dartmouth Hitchcock Medical Center, provided 30-days of RPS supplement (titration: 24 g for 15 days; 48 g for 15 days) starting 90-days post-op and assessed gastrointestinal symptoms, dietary intake (24 h recall), fecal SCFAs, and fecal gut microbiome (n = 30).

Results

The RPS supplement was well tolerated by participants (77% achieving consumption goal), with modest, but significant increases in self-reported gas (p = 0.004) and abdominal rumbling (p = 0.047). It was associated with an increase in the Bifidobacterium genus (LDA score >4, p < 0.001) and specifically, an enrichment of B. faecale/adolescentis (p = 0.006), one of two known primary degraders of RPS. No changes were observed in the abundance of cross-feeders or in SCFA concentrations. Alpha diversity measures were significantly lower post-RPS, and there was no change in species richness.

Discussion

This study demonstrated that a RPS supplement is tolerable in post-op bariatric patients and that the gut microbiome is responsive to fiber interventions. RPS increased the abundance of SCFA-producing bacteria but did not alter fecal SCFAs, likely due to competition among cross-feeders.

Keywords: bariatric surgery, butyrate, fiber, gut microbiota, obesity, potato starch, short-chain fatty acids, supplementation

1. Introduction

Over the past three decades, the rate of adult obesity has rapidly increased, such that in New Hampshire (NH), United States of America (USA), 9.9% of adults had obesity in 1990 compared to 29.9% in 2020 (1). Bariatric surgery, including Roux-en-Y gastric bypass (RYGB) and laparoscopic sleeve gastrectomy (LSG), is an effective treatment for obesity, typically yielding 50% excess weight loss and reducing long-term mortality (2). Guidelines from the National Institutes of Health recommend that bariatric surgery be offered to individuals with a body mass index (BMI) > 40 kg/m2 or a BMI of 35 kg/m2 with obesity-related co-morbidities (such as diabetes), and an estimated 270,000 bariatric surgeries are conducted annually in the USA (3).

Despite the effectiveness of bariatric surgery for weight loss, nutritional deficiencies are of great concern due to a lack of stomach volume, reduced food intake, and malabsorption (4–6). Nutritional management after surgery involves a series of diets, transitioning from liquid to pureed to soft foods, with a return to solid foods within a couple of months. Post-operative (post-op) bariatric surgery guidelines prioritize protein intake to ensure sufficient nutrients for wound healing and muscle mass maintenance, due to common experiences of early satiety (7). Fiber can be re-incorporated after bariatric surgery; however, post-bariatric populations are largely under-consuming fiber, with studies showing consumption ranging from 8–22 g/day (8–12). The Dietary Guidelines for Americans (DGA) recommend 25–34 grams of fiber (14 g/1000 calories) daily for adults; however, no clear guidelines have been established for fiber intake after bariatric surgery (13). While gastrointestinal intolerance to fiber-rich foods is a potential rationale for the decrease in fiber intake post-bariatric surgery (10), Fernandes et al. demonstrated adherence and tolerance to a low-viscosity fiber supplement, 6 g of fructo-oligosaccharides (FOS), initiated at 30-days after bariatric surgery (14).

Bariatric surgery alters the gastrointestinal environment, including the gut microbiome (15). Gut microbiome modifications are most drastic during the first 3 months after surgery, due to anatomical changes and alterations to dietary intake (15). Shifts in microbial diversity and composition are typically maintained after 3 months, with some studies showing regression toward pre-surgical composition after 1 year post-op bariatric surgery (15). Bariatric surgery has mixed impacts on the gut microbiome; with positive impacts such as increased alpha diversity, and negative impacts such as reduced straight short-chain fatty acids (SCFAs) (16–19). We have previously demonstrated that bariatric surgery is associated with a 26% decrease in SCFAs from pre- to post-surgery (~4 months), including butyrate (17).

Studies have shown that the gut microbiome and SCFA levels are responsive using short-term dietary interventions in a healthy population (20, 21). Short-term fiber interventions, such as potato starch, maize, inulin, FOS, and tapioca, in healthy populations have had consistent impacts on gut microbial composition, with enrichment of SCFA-producing taxa, including those of the Bifidobacterium genus (22). Baxter et al. demonstrated that resistant potato starch (RPS) resulted in the greatest increase in total SCFAs (32% increase), including butyrate (20). RPS is both non-bulking and non-viscous, making it an ideal choice for supplementation in a post-bariatric surgery population because they are susceptible to early satiety. In addition, RPS is moderately fermentable with the potential of modifying the gut microbiome and SCFA profiles in a post-bariatric surgery population. Modeling our clinical trial on Baxter et al., the objective of our study was to assess the impact of a 30-day RPS supplement on fecal SCFA-producing bacteria abundance and SCFA concentrations in a population that recently underwent bariatric surgery.

2. Materials and methods

2.1. Study cohort

This clinical trial (ClinicalTrials.gov, identifier: NCT05653648) was conducted at Dartmouth Hitchcock Medical Center’s (DHMC) Bariatric Surgery Program, testing compliance and gastrointestinal tolerance of a 30-day dose of RPS in bariatric patients from February 2023 through February 2024. Patients were recruited during a standard-of-care visit with the Bariatric Surgery Program Team at DHMC in Lebanon, NH, approximately two months after bariatric surgery. Inclusion criteria were as follows: (1) adults, age ≥18 years; (2) bariatric surgery utilizing RYGB or LSG; (3) the first occurrence of bariatric surgery; and (4) surgery through DHMC. At a telehealth appointment approximately 2.5 months after bariatric surgery, patients were screened for the following exclusion criteria: (1) current weight ≥550 lbs. (due to capacity of scales); (2) inadequate nutrition per standard of care guidelines as demonstrated by protein intake <60 grams per day and fluid intake <48 fl oz. per day (self-report); (3) allergies to potato starch; and (4) inability to speak and/or write in English. The study was monitored by the Dartmouth Health Human Research Protection Program (IRB; STUDY02001775), and all participants (n = 30) provided written consent.

2.2. Research study design

All patients received standard clinical care for bariatric surgery and followed a post-bariatric surgery visit schedule, with a minimal number of visits planned at 1, 4, and 12 months, then yearly. Patient information was extracted from the electronic medical record, including surgery date, surgery weight, surgeon, type of bariatric surgery, condition at discharge, 1-month follow-up date, and 1-month follow-up weight. Demographic and clinical data were collected as determined by the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) and the American Society for Metabolic and Bariatric Surgery (ASMBS) guidelines, including sociodemographic information such as sex, age, race, ethnicity, education, and income (23, 24).

The research protocol occurred between two clinical visits: Visit 1 (telehealth), taking place ~90 days post-surgery, and Visit 2 (telehealth or in-person clinic appointment), taking place ~120 days post-surgery (Figure 1). Study supplies were shipped to the participant prior to Visit 1, including two stool sample kits, 30 days of potato starch, a shaker, and a scale to measure body weight. Participants were asked to provide stool samples between 85 and 90 days post-surgery (pre-RPS) using the provided OMNIgene GUT kit (OMR-200 and ME-200 by DNA Genotek) and to return the sample to the research team via U. S. Mail. Samples collected are stable for several days at room temperature. Upon receipt at DHMC, samples were aliquoted and stored at −80 °C.

Figure 1.

Timeline infographic showing a bariatric surgery clinical study. Recruitment and consent occur pre- and post-surgery. After follow-up, participants provide a stool sample at Visit 1, then receive dose one, twenty-four grams per day, followed by dose two, forty-eight grams per day, then provide another stool sample at Visit 2, and attend follow-up appointments.

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Timeline of research protocol and resistant potato starch dosing in comparison with routine clinical care. Created in BioRender. Meijer, J. (2026) https://BioRender.com/ed6m5n5.

At Visit 1, participants completed 24-h dietary recall using the Nutritional Data System for Research (NDSR) The 24-h dietary recalls were used to assess diet food group consumption, as well as overall diet pattern using the, Health Eating Index (HEI) – 2015 score, which assess how closely a person’s dietary intake follows the DGA recommendations (25). During visit 1, an electronic survey disseminated using Research Electronic Data Capture (REDCap, RRID: SCR_003445), a secure, web-based software platform designed to support data capture for research studies, was administered regarding gastrointestinal symptoms, demographics, digestive history, and eating behaviors over the past 30 days (26). To evaluate gastrointestinal symptoms, the Gastric Symptom Rating Scale (GSRS) questionnaire was used (scored on a scale of 0–3, with three being most severe) (27, 28). The Three-Factor Eating Questionnaire (TFEQ)-R18 was used to evaluate eating behaviors among three sub-scores, with higher levels indicating higher perceived cognitive restraint, uncontrolled eating, and emotional eating (29). Participants were instructed to take a daily dose of 24 grams of potato starch (Bob’s Red Mill, Milwaukee, OR) administered via a shaker bottle mixed with a food or beverage for 15 days (Days 1–15) and complete a GSRS questionnaire on Day 3 of supplementation. On Day 16, participants were instructed to increase the dose to 48 grams daily (Days 16–30) and complete a GSRS questionnaire on Day 18. The potato starch contains approximately 70% resistant starch (type 2) by weight; therefore, subjects consumed approximately 17 grams and 34 grams of resistant starch, respectively (20). While taking the supplement, participants reported daily RPS intake and weighed themselves twice per week, with all metrics reported on a written log. Participants provided a second stool sample between 115 and 120 days post-surgery, which corresponds to day 26–30 of taking RPS dose 2 (post-RPS). Visit 2 occurred at the conclusion of the protocol and coincided with the 4-month standard of care follow-up visit. At Visit 2, participants completed an exploratory RPS preference survey (survey questions available in Supplementary Table 1) evaluating the taste and texture of RPS and the GSRS questionnaire to assess tolerance to the RPS supplement, as well as TFEQ questionnaires and a 24-h dietary recall using NDSR. Feasibility was defined as >70% of participants self-reporting ≥75% consumption of all RPS supplements and self-reported satisfaction during the RPS preference survey. Tolerance was classified as average GSRS scores ≤1, corresponding to occasional or no symptoms.

2.3. Short-chain fatty acid quantification

Targeted metabolomics was conducted by the Michigan Regional Comprehensive Metabolomics Resource Core (MRC2). Fecal samples were pre-weighed, homogenized via a probe sonicator, and centrifuged at 16000 rpm for 10 min. Supernatants were extracted with acetonitrile, and an internal standards mixture was used to create standard curves. The analytical platform was liquid chromatography/mass spectrometry (Agilent 6,410 QQQ) with mobile phase A: water + 0.1% formic acid and mobile phase B: methanol + 0.1% formic acid. Acetate, propionate, butyrate, valerate, caproate, hexanoate, isobutyrate, and isovalerate were profiled and normalized to account for the weight of feces.

2.4. Gut microbiota composition

Fecal samples were provided to the Marine Biological Laboratory in Woods Hole, MA, and the 16S rRNA V4 region was sequenced using the MiSeq. Demultiplexed FASTQ files were provided for further processing via QIIME2 (RRID: SCR_021258), DADA2 (RRID: SCR_023519) validated pipelines, using the SILVA 138.2 Ref NR 99 database for taxonomic classification (30). To avoid sequencing depth bias, Annotated amplicon sequencing variant (ASV) tables were rarefied to an even depth, and samples with < 20,000 reads were excluded prior to analysis. ASV tables were analyzed in RStudio using the phyloseq (RRID: SCR_013080) package for abundance analyses and quantification of microbiome diversity (alpha diversity, richness, and beta diversity) (31). Sequences of interest (SCFA-producing species, including primary and cross-feeders) were identified using the NCBI 16S rRNA sequence database (32). SCFA producers with identical V4 regions (i.e., Bifidobacterium adolescentis, B. faecale, and B. stercoris) were grouped as one species for the purposes of this analysis (20). Targeted analyses identified R. bromii, B faecale/adolescentis, B. longum/breve, B. catenulatum/pseudocatenulatum/kashiwanohense, B. bifidum, and R. intestinalis abundances. Microbial composition was compared pairwise pre- and post-RPS and correlated to fecal SCFA concentrations.

2.5. Statistical analysis

Paired t-tests (t.test) were used to identify differences in GI symptoms, dietary data, and TFEQ sub-scores by pre- and post-RPS. To identify differences in alpha diversity metrics after the intervention, paired Kruskal-Wallis tests were used. To assess differences in genus abundance after the RPS intervention, mixed linear models were utilized (MaAsLin3) with participant as the random effect (33). As a secondary analysis, a linear discriminant analysis (LEfSe) was performed to identify differences in bacteria (genus level) abundance according to pre- and post-RPS using the microeco package (34). A targeted analysis of SCFA species via NCBI BLAST (RRID: SCR_004870) was conducted among the top 500 prevalent ASVs, mirroring Baxter et al. (20). Pseudo counts of one were utilized in calculating fold changes of species abundance to account for zero inflated microbiome data. To assess increases in targeted taxa, paired, one-sided, Wilcoxon tests were conducted (wilcox.test). To determine if the levels of SCFA-producing bacteria abundance are associated with SCFA concentrations, Spearman correlation (rcor.test) and multivariable linear regression controlling for false discovery rate (FDR) were conducted. To determine if the potato starch intervention significantly altered levels of SCFAs, paired t-tests (t.test) and multivariable linear regression (lm) were conducted. For regressions, covariates that were considered include age, sex, weight at bariatric surgery, dietary starch, and fiber intake from 24-h recalls, and adherence to starch protocol. All statistical analyses were performed using R via RStudio 4.4.0.

3. Results

3.1. Population characteristics

Most participants were women (83%) with a self-reported white race (93%) and non-Hispanic ethnicity (97%). The median (Q1, Q3) age of participants was 47 years (39 years, 56 years). The majority of participants (77%) received a RYGB (Supplementary Table 2). The median pre-bariatric surgery weight was 272.6 lbs. (249.7, 306.2). Participants lost weight during the duration of the RPS, with a median 7 lbs. (5.1, 8.8) loss during the intervention (Supplementary Figure 1).

3.2. Potato starch intervention tolerance

Overall, participants (n = 30) had an agreeable opinion of the RPS dissolvability, consistency, taste, and amount (Supplementary Table 1). Participants were neutral on their enjoyment of taking RPS (Supplementary Table 1). Notably, 77% of the cohort self-reported that they consumed ≥75% of all RPS supplements, and there was a trending association that participants who enjoyed taking RPS were more likely to have consumed a higher percentage of the RPS during the study (r = 0.36, p-value = 0.06). All average GSRS symptom scores were ≤1 pre- and post-bariatric surgery (data not shown). However, there was a significant self-reported increase in gas (p = 0.0004) in response to the RPS supplement, with average symptoms tracking from “normal” to “occasional discomfort for a short duration” (Figure 2). There was a significant self-reported increase in abdominal rumbling (p = 0.047) and a trend toward increases in abdominal pain (p = 0.083), bloating (p = 0.054), and increased stool frequency (p = 0.083). There were no differences in heartburn, acid reflux, nausea and vomiting, belching, stool consistency, urgent defecation, or constipation in response to the RPS supplement.

Figure 2.

Bar charts illustrate abdominal gas and abdominal rumbling symptom scores at pre-RPS, Day 3, Day 18, and post-RPS supplementation, showing significant increases for both symptoms. Additional symptoms trend towards increases or show no significant change.

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The Gastrointestinal Symptom Rating Scale, to assess supplement tolerance, was administered pre-resistant potato starch, on day 3, day 18, and post-resistant potato starch. Gas and abdominal rumbling symptoms increased significantly after 30 days of potato starch supplementation, as indicated by a paired t-test.

3.3. Dietary changes

There were no self-reported significant differences in protein, starch, or fiber intake in response to RPS supplement (Supplementary Table 3). A majority of participants did not meet DGA fiber recommendations (25–34 grams/day), with 100% of the participants pre-RPS (median: 7 grams) and 92% of the participants post-RPS consuming less than 25 grams of fiber daily (median: 10 grams) (13). Protein intake was greater than or equal to 60 grams (ASMBS guidelines) for 62% of the participants pre-RPS (median: 63 grams) and 73% of the participants post-RPS (median: 71 grams). There was no significant difference in Healthy Eating Index Score (HEI)-2015 in response to the potato starch supplement (25). Using TFEQ sub-scores, the overall population had moderately high cognitive restraint (median: 61.1) and low levels of uncontrolled (median: 3.7) or emotional eating (median: 5.6) at pre-RPS. TFEQ sub-scores were similar pre- and post-RPS intervention, with no significant eating behavior changes noted (Supplementary Table 3).

3.4. Microbiome changes

3.4.1. Decreases in alpha diversity

Of the 30 participants, 26 had both pre- and post-stool samples, with two excluded due to low sample depth (n = 24). The alpha diversity metrics, Shannon (t-stat = 3.62, p = 0.001) and Inverse-Simpson (t-stat = 3.28, p = 0.003), were significantly lower post-RPS (Figure 3A). Observed richness (t-stat = 1.50, p = 0.15) and Faith PD (Kruskal-t-stat = −0.05, p = 0.96) were not significantly different, indicating observed decreases in alpha diversity could likely be attributed to a decrease in taxonomic evenness. Beta-diversity was not significantly different after RPS supplementation (p = 0.42) (Supplementary Figure 3).

Figure 3.

Panel A displays four box plots comparing Faith PD, Observed Richness, Shannon Diversity, and Inverse-Simpson Diversity for microbiome samples pre- and post-RPS intervention, indicating significant decreases post-RPS. Panel B presents a stacked bar chart of average relative abundance by bacterial genus, showing a significant reduction in a major group after RPS, with genus color codes in the legend. Panel C features six line plots for different primary degraders, cross-feeders, and a butyrate producer, illustrating individual sample changes in percent abundance before and after RPS, with B. faecale/adolescentis showing a notable increase.

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Changes in microbiome composition in response to resistant potato starch supplement. (A) Alpha diversity according to pre-resistant potato starch (Pre-RPS) and post-resistant potato starch (Post-RPS). There were no observed changes in Faith PD or observed richness. Shannon and Inverse-Simpson decreased significantly post-resistant potato starch (**, p < 0.01 by paired Wilcoxon test) (B) Average relative abundance of the top 15 most abundant genera according to pre- and post-resistant potato starch. Bifidobacterium was found significantly enriched post-resistant potato starch (***, p < 0.001 by untargeted linear discriminant analysis effect-sized). (C) Average relative abundance of resistant potato starch degraders and cross-feeders identified from targeted BLAST analysis of the top 500 most abundant ASVs pre- and post-resistant potato starch. B. faecale/adolescentis significantly increased post-resistant potato starch (**, p < 0.01 by one-sided paired Wilcoxon test).

3.4.2. Enrichment in Bifidobacterium genus

Bifidobacterium was significantly enriched (β = 3.53, p = 0.0039) after RPS supplementation in a mixed linear model with participant as the random effect, however, significance was lost after BH correction. In a secondary analysis, LDA effect size (LEfSe) comparison of taxonomic abundance on the genus level also identified Bifidobacterium as significantly enriched post-RPS supplementation (p < 0.001), with observed increases in 18 out of 24 participants included in this analysis (Figure 3B; Supplementary Figure 4). No significant differences were observed in ASV relative abundance amongst the 500 most abundant ASVs. A targeted BLAST analysis identified specific SCFA-producing species. The percent abundance of B. faecale/adolescentis was significantly greater post-RPS supplement (V = 62, p-value = 0.006) (Figure 3C). Bifidobacterium was measurably increased post-RPS; however, it was not correlated to post-RPS SCFAs. Only valerate showed a significant negative correlation with Anaerostipes (R = −0.51, p = 0.049) abundance after post-RPS, after controlling for FDR.

3.4.3. Classification according to Bifidobacterium response

Exploratory analysis classified participants as Bifidobacterium responders (% relative abundance change >0, n = 18) and non-responders (% relative abundance change ≤0, n = 6). There were no statistically significant differences in changes in SCFAs or percent change of SCFAs according to Bifidobacterium responder vs. non-responder status. Bifidobacterium responder status was also not significantly associated with surgery type or pre- and post-dietary data (protein, starch, fiber, and HEI). Furthermore, baseline microbiome composition was not related to Bifidobacterium responder status.

3.5. Changes in fecal SCFA

No changes in SCFA concentrations were observed pre- vs. post-RPS. Baseline SCFA concentration was not related to surgery type, sex, total fiber intake, or starch intake (data not shown). Only one SCFA, acetate, was correlated with pre-RPS weight (cor = 0.44, p = 0.02) (Table 1). No other SCFAs were related to any weight metrics.

Table 1.

Short-chain fatty acids: pre- vs. post-resistant potato starch.

Short-chain fatty acid Pre-resistant potato starch Post-resistant potato starch Percent change Paired t-test
n mean median (Q1, Q3) n mean median (Q1, Q3) median % (Q1, Q3) t-statistic p-value
Acetate (nmol/mg) 30 51.65 46.5 (34.4, 59.5) 26 51.0 51.1 (35.5, 65.0) 9.15(−28.3, 57.5) 0.27 0.79
Propionate (nmol/mg) 30 14.88 13.9 (10.2, 17.3) 26 13.7 12.7 (7.9, 19.0) −15.3(−44.4,41.8) 1.24 0.23
Isobutyrate (nmol/mg) 30 2.25 2.0 (1.4, 2.5) 26 1.8 1.6 (1.2, 2.1) −16.6(−44.7, 24.7) 1.21 0.24
Butyrate (nmol/mg) 30 9.80 8.2 (6.9, 13.1) 26 11.3 8.8 (5.4, 14.6) 15.1(−33.4, 80.9) −0.15 0.88
2-methylbutyrate, putative (nmol/mg) 30 0.84 0.7 (0.4, 1.1) 26 0.8 0.6 (0.4, 1.0) −11.5(−50.7, 51.0) 0.094 0.93
Isovalerate (nmol/mg) 30 1.48 1.3 (0.9, 1.8) 26 1.3 1.0 (0.8, 1.4) −15.1(−53.2, 68.9) 0.92 0.36
Valerate (nmol/mg) 30 2.77 2.0 (1.7, 3.3) 26 2.2 1.6 (1.0, 2.9) −29.5(−59.0, 15.4) 0.89 0.38
Total SCFA (nmol/mg) 30 83.67 72.9 (60.7, 99.7) 26 82.01 81.3 (57.0, 100.2) 1.0(−27.1, 56.6) 0.38 0.71
Branched SCFA (nmol/mg) 30 3.73 3.2 (2.6, 4.45) 26 3.09 3.09 (2.0, 3.5) −21.2(−50.2, 28.6) 1.27 0.22
Straight SCFA (nmol/mg) 30 79.94 70.5 (57.4, 92.6) 26 78.91 79.0 (54.3, 98.2) 4.47(−27.3, 54.9) 0.34 0.74
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Paired t-tests were performed on log10 transformed SCFAs concentrations for n = 26.

4. Discussion

We tested the feasibility and tolerance of a 30-day RPS supplement to alter fecal SCFAs and the abundance of SCFA-producing bacteria in a post-bariatric surgery population. RPS was selected as a low-viscosity fiber with the potential to alter the microbiome, and evidence suggests it could increase butyrate production (20). In the present study, RPS altered the gut microbiome, but did not impact fecal SCFA concentrations (Figure 4).

Figure 4.

Flowchart summarizes a study where participants consumed 24 or 48 grams of Bob’s Red Mill Potato Starch, with 77 percent adherence. Increased abdominal rumbling and gas were noted. Microbiome outcomes showed increased B. faecale/adolescentis and no significant change in R. bromii, B. longum/breve, B. bifidum, B. catenulatum/pseudocatenulatum/kashiwadanii, R. intestinalis, acetate, propionate, or butyrate. Potential influences include competition, pH, and SCFA utilization.

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Flowchart of study intervention and key findings. The 30-day, two-step dose, resistant potato starch intervention (RPS) resulted in high adherence with some gastrointestinal side effects. The resistant potato starch intervention altered the microbiome, specifically increasing the abundance of primary degrader B. adolescentis. However, there were no changes in the identified cross-feeders or in short-chain fatty acids (acetate, butyrate, propionate).

A significant decrease in diversity was observed in response to RPS supplementation. Diversity metrics, including Shannon and Simpson, combine measures of bacterial richness and evenness to characterize the composition of the gut microbiome (35). Lower gut microbial diversity is often an unfavorable outcome, but within fiber supplementation studies, it can be a sign that the intervention is effective at increasing the abundance of targeted bacteria (22, 36). There was no decrease in microbial richness or Faith PD, demonstrating that phylogenetic diversity, or the number of microbial members, was maintained, but rather the evenness of bacterial abundances was altered by RPS supplementation.

After the RPS intervention, we saw a notable increase in the genus Bifidobacterium, which has been previously demonstrated in other fiber interventions (RPS, inulin, and FOS) (22, 36). Furthermore, in a targeted analysis, we identified the B. faecale/adolescentis as significantly enriched post-RPS. We cannot distinguish B. faecale from B. adolescentis within this 16S rRNA analysis; however, B. adolescentis is a primary degrader of RPS and likely driving the observed increase in abundance. Primary degraders of RPS are pivotal, as they process RPS into accessible intermediates for cross-feeders to utilize. Currently, only two known primary degraders of RPS in humans have been identified: R. bromii and B. adolescentis (37) (Figure 4). Significant amounts of energy are directed to these primary degraders during a RPS intervention, often resulting in increased abundances (36). While R. bromii abundance increased in some participants post-RPS (change in % relative abundance >0, n = 5, Figure 3C), only increases in B. faecale/adolescentis were significant. B. adolescentis can produce a mixture of lactate, acetate, ethanol, and carbohydrates from RPS degradation, which are used by cross-feeders in butyrate production (37, 38). The primary degraders, B. adolescentis and R. bromii, are critical to initiating RPS breakdown, but their presence alone is insufficient for producing butyrate.

Cross-feeders utilize carbohydrates and acetate from the primary degrader RPS fermentation to further process into SCFAs. R. intestinalis was the only butyrate-producing cross-feeder identified in our targeted analysis, and its abundance did not significantly increase post-RPS. Our targeted analysis identified other cross-feeders, including B. longum/breve, B. bifidum, and B. catenulatum/pseudocatenulatum/kashiwanohense, which are actively competing for the same RPS breakdown products as R. intestinalis and producing other SCFAs like acetate, rather than butyrate (20). Our analysis did not observe any changes in the abundance of non-butyrate-producing cross-feeders, likely due to complex cross-feeding interactions, in which many bacteria are competing for the same resources (i.e., RPS breakdown products, lactate, acetate, ethanol) (38, 39). The spread of resources between many bacteria likely resulted in an unpredictable cross-feeder response and the lack of consistent changes in SCFA production. While there were likely other cross-feeders present, our study was limited to 16 s rRNA sequencing, and future studies would benefit from incorporating whole genome sequencing for improved species annotations. For example, E. hallii is a common low-abundance cross-feeder in humans but likely missed in our targeted analysis. E. hallii relies on lactate produced by B. adolescentis from RPS to produce butyrate (40).

Gathering results from Baxter et al. and our previous work, we hypothesized that RPS would increase butyrate production in post-op bariatric patients (17, 20). Our hypothesis was not supported by the results of this study, suggesting that dynamics may differ after bariatric surgery due to altered gastrointestinal conditions, microbial community relationships, or SCFA utilization. Additionally, Baxter et al. yielded increases in butyrate following a two-week RPS intervention in healthy adults, however, the 30-day RPS intervention duration may have been too short to detect measurable metabolic changes after bariatric surgery (20). pH has been shown to sway propionate and butyrate production and should be incorporated as a metric in future studies, given changes in gastrointestinal pH after bariatric surgery (41, 42). Nuanced microbial community relationships could have also influenced SCFA production. For example, increases in R. bromii (primary degrader) paired with E. rectale (cross-feeder) have been associated with a higher production of butyrate after the consumption of RPS (20). While our study assessed increases in specific bacteria, it did not consider the complex co-dependencies among microbial relationships that are needed to produce butyrate. Changes in SCFA utilization or absorption could be attributed to the lack of increased fecal SCFA levels, but remain difficult to monitor in vivo (18, 20, 38). Intestinal barrier function is often reduced in obesity and improves after bariatric surgery (43). Colonocytes that make up the intestinal barrier preferentially use butyrate as a fuel source; however, there has been no study investigating colonocyte usage of SCFAs after bariatric surgery or significant weight loss. Our findings suggest that while RPS is successful in altering B. adolescentis abundance post-op, it has unpredictable effects on cross-feeders and fecal SCFAs. Future directions include a randomized control trial with additional stool samples, pre-op bariatric surgery and post-RPS (45-day), to delineate the effects of RPS on the gut microbiome, SCFAs, and gastrointestinal symptoms (14).

Incorporating a RPS supplement has the potential to fill a dietary gap common in patients who have undergone bariatric surgery. Overall, many patients experience post-bariatric surgery increases in abdominal pain and constipation; therefore, patients reduce fiber intake for fear of exacerbating symptoms (27, 44). Within our study, the endpoint mean fiber intake (excluding RPS) was only 12 grams/day, less than half of the daily DGA recommendation (13, 45). Our study demonstrated that a RPS supplement could be feasible and tolerated post-op bariatric surgery following a two-step dosing regimen. While participants were neutral on the enjoyment of taking RPS, our study showed high adherence, indicating RPS supplementation is feasible post-bariatric surgery. Average gastrointestinal symptoms scores remained ≤1 (data not shown) throughout the supplementation and symptoms were particularly mild at the initial dose of 24 grams/day (20). However, gas and abdominal rumbling symptoms trended upward as the dose increased to 48 grams/day and increased significantly by the end of the study, after 30 days of RPS. Other studies suggest incorporating ~15 grams/day of RS for gastrointestinal and metabolic health improvements, and a dosage of 3.5 grams for 4 weeks was still shown to enrich Bifidobacterium in healthy adults (46, 47). Future studies may benefit from implementing a smaller dose or a slower dose buildup (e.g., starting at 25% of the goal dose and increasing every 2 weeks for an 8-week buildup) to ease GI symptoms (46, 48, 49).

Limited dietary intake and medication use data, in the absence of a control group, was a major limitation of the study. Our study incorporated only one pre-RPS and one post-RPS 24-h recall, which allowed for the quantification of short-term caloric, protein, starch, and fiber intake. Future studies should consider repeat 24-h recalls (2 weekday and 1 weekend) at multiple timepoints paired with a Food Frequency Questionnaire (FFQ) geared at accessing fiber intake and a quantification of fluid intake (50). After bariatric surgery, patients are instructed to avoid drinking during meals to avoid early satiety, making it challenging for patients to consume adequate amounts of fluid. Ensuring adequate fluid intake is necessary to avoid exacerbating GI symptoms from fiber intake. Medication use, particularly medications taken frequently after bariatric surgery including probiotics, proton pump inhibitors (PPI), laxatives, and vitamin supplementation, can alter the gut microbiome (51–54). We observed self-reported trends of decreased laxative use among a portion of participants (data not shown). Future studies should screen for antibiotic use as an exclusion criterion and consider quantitative metrics on stool softeners and laxatives, probiotics, PPIs, and vitamins.

To fill a dietary gap, RPS was selected for its non-bulking, non-viscous properties and its potential to modify the gut microbiome and increase SCFA production. RPS was successful in increasing the abundance of a SCFA producer, but did not yield changes in SCFAs, as seen in healthy populations (20). Future studies in bariatric surgery populations should utilize a control group to identify changes to the microbiome exclusively attributed to RPS, as natural temporal variation, dietary changes, medication use and post-surgical adaptations could have confounded current observations. Measures of pre-surgery microbiome composition, pH alterations, and host SCFA utilization in future research will clarify observed pathways. Additionally, pairing a smaller dose of RPS with other low-viscosity fibers, such as acacia gum, could provide a better opportunity for SCFA production, as a variety of fiber sources is often seen in habitual diets with higher fiber consumption (55). Currently, there are no studies identifying consistent increases in SCFAs from a particular combination of dietary fibers after bariatric surgery. Despite a lack of SCFA level increases, this study found that patient’s post-bariatric surgery could tolerate low-viscosity fiber supplementation and that the gut microbiome post-op is responsive to RPS interventions.

Acknowledgments

Thank you to the New Hampshire Agricultural Experiment Station, the Hitchcock Foundation at Dartmouth Hitchcock Medical Center, and Happy Trails Nutrition L.L.C.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the New Hampshire Agricultural Experiment Station Hatch program, project award number (NH00703), from the US Department of Agriculture‘s National Institute of Food and Agriculture and the state of New Hampshire, Hitchcock Foundation at Dartmouth Hitchcock Medical Center (2023–2024), and Happy Trails Nutrition L.L.C. The funder was not involved in the study design, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

Footnotes

Edited by: Evangelia Kotzakioulafi, University of Zurich, Switzerland

Reviewed by: Darrell W. Cockburn, The Pennsylvania State University (PSU), United States

Kingsley Chukwudozie, University of Nigeria, Nsukka, Nigeria

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, PRJNA1474458.

Ethics statement

The studies involving humans were approved by Dartmouth Health Human Research Protection Program. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

BM: Data curation, Conceptualization, Visualization, Formal analysis, Writing – original draft, Software. SS: Formal analysis, Writing – review & editing. SLe: Writing – review & editing, Data curation, Project administration. JL: Writing – review & editing, Investigation. SLa: Writing – review & editing, Investigation. MM: Writing – review & editing. TT: Writing – review & editing, Investigation. EH: Writing – review & editing, Investigation. SB: Investigation, Conceptualization, Writing – review & editing, Funding acquisition. DG-D: Methodology, Conceptualization, Writing – review & editing, Funding acquisition. MD: Writing – review & editing, Formal analysis. JM: Supervision, Funding acquisition, Formal analysis, Methodology, Writing – original draft, Conceptualization.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Publisher’s note

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2026.1912097/full#supplementary-material

Data_Sheet_1.pdf (386.1KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data_Sheet_1.pdf (386.1KB, pdf)

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, PRJNA1474458.

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