A Systematic Review of the Effects of Diet on the Gut Microbiota in Individuals at Risk for Colorectal Cancer

A Systematic Review of the Effects of Diet on the Gut Microbiota in Individuals at Risk for Colorectal Cancer | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search A Systematic Review of the Effects of Diet on the Gut Microbiota in Individuals at Risk for Colorectal Cancer Saurabh Pandey , Jenna Skidmore , Jaapna Dhillon doi: https://doi.org/10.1101/2025.04.14.25325698 Saurabh Pandey 1 Department of Nutrition and Exercise Physiology, University of Missouri , Columbia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jenna Skidmore 1 Department of Nutrition and Exercise Physiology, University of Missouri , Columbia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jaapna Dhillon 1 Department of Nutrition and Exercise Physiology, University of Missouri , Columbia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: jdhillon{at}missouri.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Colorectal cancer (CRC) poses a major public health concern, with emerging evidence highlighting the critical role of the gut microbiota in CRC progression. Considering that diet is a major modifiable factor influencing CRC risk, partly through its interaction with the gut microbiome, this systematic review evaluated the effects of dietary intervention RCTs, including supplements, functional foods, whole foods, and dietary patterns, on the gut microbiome in at-risk individuals (PROSPERO: CRD42024530038). A search of PubMed, Scopus, and Web of Science identified 4,746 records, with 20 additional records sourced manually. Following screening, six studies met inclusion criteria, focusing on microbial diversity, taxonomic composition, and metabolites. Findings demonstrate that whole food interventions like navy beans and functional foods like rice bran increased microbial diversity over 4-8 wk; with no effects of rice bran over 24 wk. Navy beans (8 wk) enriched beneficial taxa ( Faecalibacterium , Bifidobacterium ), while a shorter 4-wk intervention using bean powder showed no taxonomic shifts. Both forms of navy bean increased amino acid derivatives and anti-inflammatory phenolic metabolites. Rice bran increased Lactobacillus over 24 wk but showed differing effects on Firmicutes over short (2 wk) versus longer (24 wk) durations. Rice bran also increased SCFAs (acetate, propionate) over 2 wk and phenolic metabolites (e.g., diosmin) while reducing pro-carcinogenic byproducts (p-cresol sulfate, glycodeoxycholate) over 4 wk. β-glucan-enriched bread increased acetate over 12 wk but had minimal effects on microbial composition. Healthy Eating and Mediterranean dietary patterns did not alter taxonomic composition over 26 wk but reduced branched-chain bacterial fatty acids, indicating reduced proteolytic fermentation. This review underscores the potential of dietary strategies to modulate the gut microbiome in CRC-risk populations. However, limited RCTs and heterogeneity limit generalizability. Future research should conduct rigorous RCTs across the lifespan, using advanced microbiome and metabolite analyses and examining understudied dietary patterns to guide CRC prevention. Introduction Colorectal cancer (CRC) is one of the most frequently diagnosed cancers globally and accounts for a considerable proportion of cancer-associated illness and death, creating a substantial public health burden ( 1 ). Recent projections indicate a dramatic rise in colorectal cancer incidence among young adults, with expected increases of up to 124% for those aged 20–34 and nearly 46% for individuals aged 35–49 by the year 2030 ( 2 ). Globally, CRC incidence is three to four times higher in developed countries than in developing regions, reflecting its association with socioeconomic development ( 3 ). Diet plays a pivotal role in CRC risk influencing its development through interactions with the gut microbiome ( 4 ). Adherence to dietary patterns such as the Mediterranean diet, Healthy Eating Index-2005, or prudent dietary patterns, and other patterns rich in fruits, vegetables, and whole grains, is associated with reduced CRC risk (relative risk [RR] 0.45–0.90) ( 5 ). Conversely, dietary patterns dominated by processed and red meats, refined foods, and Western diets are linked to significantly increased CRC risk (RR 1.18–11.7) ( 5 ). Similarly, cohort and case-control studies in young adults at risk for early-onset CRC show that diets high in sugar-sweetened beverages, deep-fried foods, and low in folate and fiber are associated with increased cancer risk, while fruit and vegetable-based diets rich in micronutrients are protective ( 6 ). The gut microbiome mediates diet-related effects on CRC risk. Dysbiosis, or a disruption in microbial balance, contributes to chronic inflammation, genotoxic metabolite production, and immune dysregulation, all of which are implicated in CRC development ( 4 , 7 , 8 ). For instance, high fat diets have been shown to stimulate specific gut microbiota to produce carcinogenic secondary bile acids, which can promote CRC development ( 9 ). In contrast, short chain fatty acids (SCFAs), produced from the fermentation of dietary fibers, have demonstrated protective effects against CRC, suggesting that a high-fiber diet may mitigate cancer risk ( 9 , 10 ). Recent advancements in metagenomics and metabolomics have further elucidated these diet-microbiome-CRC links. Meta-analyses of metagenomic datasets from various geographic regions have identified globally associated CRC microbes, including Fusobacterium nucleatum , Peptostreptococcus stomatis , and Gemella morbillorum , among others such as Bacteroides fragilis , Parvimonas spp., Clostridium symbiosum , and Solobacterium moorei ( 11 ). These microbial shifts are associated with CRC-enriched predicted metabolites such as amino acids, cadaverine, and creatine ( 11 ), as well as secondary bile acids ( 12 ). Additionally, functional analyses have highlighted CRC-enriched pathways, such as protein and mucin catabolism, alongside a depletion in carbohydrate degradation pathways ( 12 ). These findings underscore the complex interplay between dietary factors, gut microbiota, and CRC development. Despite these advances, significant gaps remain in understanding how specific dietary interventions influence the gut microbiome in at-risk populations. Prior systematic reviews have largely focused on diet and microbiome outcomes in general populations, diet and CRC risk, or observational studies ( 6 , 13 – 22 ). This systematic review addresses this gap by synthesizing evidence from RCTs examining dietary interventions such as whole foods, enriched foods, supplements, and dietary patterns and their impact on microbial diversity, taxonomic composition, and metabolite production. By clarifying these relationships, this review aims to inform CRC prevention strategies, guide future research, and support evidence-based dietary recommendations for at-risk populations. Methods The study protocol is registered with PROSPERO, the International Prospective Register of Systematic Reviews (CRD42024530038). Search strategy A search strategy, developed in accordance with the Cochrane Handbook of Systematic Reviews ( 23 ), is detailed in Supplementary Table S1 . The search was designed to identify dietary interventions examining the gut microbiota in individuals at risk for colorectal cancer. Primary searches were conducted on April 09, 2024 with no restrictions being imposed on the date of publication. Study selection The search was conducted in three databases (PubMed, Scopus, and Web of Sciences) and the results were compiled in Zotero (version 6.0.23). To identify additional relevant studies, reference lists of related systematic reviews and meta-analyses were manually reviewed. Three authors (JD, SP, JS) systematically assessed the articles for inclusion. Discrepancies were resolved through a voting process. Details of the PICO framework can be found in Supplementary Table S2 . The study selection process is illustrated in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram ( 24 ) ( Figure 1 ). The exclusion process was conducted in four stages. In the first pass, studies were excluded if they were duplicates, involved children, pregnant individuals, non-human subjects, or in vitro experiments. Reviews, study protocols, non-research articles, genome analyses, text-mining, food nutrient analysis, food quality studies, and studies where the full text or abstract was not available in English were also excluded. Download figure Open in new tab Figure 1: PRISMA Flow Diagram Summarizing the Study Selection Process. Two studies analyzed had multiple associated papers. *, 2 studies had multiple papers In the second pass, exclusions included studies focusing on interventions other than diet, such as exercise, drugs, probiotics (including foods classified as probiotics by the study), synbiotics, surgery, or enteral nutrition. Dietary supplements described as drugs or registered with the FDA or international regulatory agencies as drugs were also excluded. Studies where dietary effects could not be isolated, such as those involving diets provided alongside or immediately after exercise, surgery, probiotics, drugs, or colon preparation interventions, were excluded. Diets provided postoperatively within six months of surgery, where surgical effects could confound outcomes, were also excluded. Observational studies, case-control studies, case reports, and studies unclear about whether they were observational or dietary interventions were removed. In the third pass, studies were excluded if the population were healthy without colorectal cancer risk or if colorectal cancer risk was classified solely based on diet. Studies were also excluded if they did not provide microbiome data related to taxonomic composition (strain level or higher) or diversity. Additionally, studies presenting microbiome data only at the ASV/OTU level without aggregation to taxonomic composition were excluded. In the fourth pass, exclusions were based on whether the study addressed the research question. Studies examining preoperative dietary interventions effects on postoperative outcomes were excluded. Studies were excluded if no microbiome data were quantified or statistically analyzed; if microbiome data were only presented at baseline with no intervention response data presented for comparison; or if microbiome data were only presented at the end of the intervention without baseline data for comparison. Additional exclusions included studies without a comparator or control group/condition, studies without clear reporting of differences in microbial diversity or taxonomic abundances over time or between groups. Studies that reported only correlations were also excluded. Papers replicating information already presented in another paper from the same study were not included in the review. Data extraction Data collection tables were designed and finalized by three authors (JD, SP, JS). Data extraction was performed by SP and JS, with JD reviewing for accuracy. Study quality was assessed using the Oxford Centre for Evidence-Based Medicine Levels of Evidence ( 25 ), and risk of bias was evaluated using the Academy of Nutrition and Dietetics quality control checklists ( 26 ). Results In total 4746 records were screened for inclusion from database search and 20 from meta-analyses, systematic reviews, and a manual review of reference lists. A total of 4762 records were excluded based on the established criteria ( Figure 1 ). Six studies with dietary interventions that discussed microbiome outcomes in individuals at risk for CRC were included in the systematic review. Two studies had multiple publications discussing different microbiome outcomes ( 27 – 31 ) and are grouped together in Table 1 . View this table: View inline View popup Table 1: Characteristics of Studies Examining Dietary Interventions and Gut Microbiota in Individuals at Risk for Colorectal Cancer Study design and duration Four studies were conducted in the United States ( 32 – 35 ), 1 in China ( 36 ), 1 in Greece ( 37 ). Included studies varied in design and intervention duration tested ( Table 1 ). Five studies ( 27 – 31 , 34 – 38 ) used parallel-arm randomized controlled trial (RCT) designs while one study ( 32 ) employed a randomized crossover design. Study durations ranged from 4 wk to 156 wk. Dietary interventions The interventions studied were diverse ranging from dietary supplements such as ginger extract ( 33 ), functional foods such as rice bran ( 36 ) and β-glucan-enriched bread ( 37 ), whole foods such as navy beans ( 32 ), enriched meals and snacks containing rice bran or whole navy bean powder ( 35 ), and dietary patterns such as healthy eating or Mediterranean dietary patterns ( 34 ). Population characteristics The studies examined diverse populations with varying CRC risks and treatment histories. Participants included individuals with adenomatous polyps ( 37 ), colorectal adenomas ( 33 ), and precancerous colon or rectal polyps ( 32 ). Other studies defined risk based on clinical tools such as the Asia-Pacific Colorectal Screening Tool ( 36 ) or family or personal history of adenomas or colon cancer ( 27 , 28 , 34 ). Several studies included CRC survivors more than four months post-treatment for chemotherapy or radiation ( 29 – 31 , 35 ). Participants across studies were predominantly middle-aged to older adults with mean ages ranging from the 50s to 70s and mean BMIs in the overweight and obesity range. Effects of dietary intervention on microbial diversity Navy bean consumption increased stool microbial diversity, with significant increases in diversity indices such as the inverse Simpson index over 8 wk ( 32 ) and Chao index over 4 wk ( 35 ). Similarly, rice bran interventions showed an increase in Chao, Sobs, and Simpson diversity indices over 4 wk ( 35 ), but no differences in diversity over 24 wk ( 36 ). Dietary patterns such as Healthy Eating and Mediterranean diets had no significant impact on colon mucosal microbiome diversity. However, a decrease in community θYC distance, indicating reduced dissimilarity, was observed in the Healthy Eating group over 26 wk ( 34 ). Ginger extract supplement did not result in significant differences in alpha or beta diversity ( 33 ). The β-glucan intervention did not assess diversity indices ( 37 ). Effects of dietary intervention on microbial taxonomic composition Navy bean consumption over 8 wk resulted in increased abundance of Faecalibacterium , Odoribacter , Bifidobacterium , and Eubacterium brachy , with decreases in Collinsella and Ruminococcus torques ( 32 ). However, in another intervention, whole navy bean powder showed no changes in taxonomic composition over 4 wk ( 35 ). In the same study, rice bran increased the abundance of Bacteroidetes while decreasing Firmicutes, leading to a reduction in the Firmicutes-to-Bacteroidetes ratio at 2 wk compared to baseline ( 35 ). In a 24 wk duration rice bran intervention, a greater increase in Firmicutes and Lactobacillus compared to rice powder was observed ( 36 ). The ginger extract intervention resulted in significant decreases in Bacteroides , Akkermansia , and Ruminococcus compared to placebo over 6 wk ( 33 ). The β-glucan intervention decreased total coliform abundance over 4 wk and Clostridium perfringens abundance from 4 to 12 wk ( 37 ). No significant changes in taxonomic composition were observed with Healthy Eating or Mediterranean diets in colon mucosal microbiota ( 34 ). Effects of dietary intervention on microbial-produced metabolites Navy bean consumption over 8 wk increased pipecolic acid and trigonelline while decreasing an indole derivative ( 32 ). In the other navy bean intervention, stool cadaverine and 4-hydroxyphenylacetate increased while caprylate decreased over 4 wk ( 29 ). Additionally, stool hydantoin-5-propionic acid ( 29 ), plasma 3-(4-hydroxyphenyl)propionate, and plasma 2,3-dihydroxy-2-methylbutyrate were higher at 4 wk compared to control ( 30 ). Urine metabolites, including N2,N5-diacetylornithine, 4-hydroxyhippurate, and salicylate, increased over 4 wk ( 30 ). In the same study, rice bran increased stool acetic and propionic acids over 2 wk compared to control ( 35 ) and decreased stool N-acetylhistamine, glycodeoxycholate, and p-cresol sulfate over 4 wk ( 31 ). Additional increases in stool beta-hydroxyisovalerate, enterolactone, and diosmin were observed over 4 wk ( 31 ). The β-glucan intervention showed no significant changes in total volatile fatty acids (VFA), propionate, branched VFAs, or other-VFA concentrations. However, higher acetate concentrations were observed at 12 wk compared to placebo ( 37 ). A decrease in total branched-chain bacterial fatty acids ( 28 ) but no changes in trimethylamine-N-oxide (TMAO) ( 27 ) or lipopolysaccharide-binding protein (LPS-binding protein) ( 28 ) were observed in Healthy Eating and Mediterranean diets over 26 wk. Quality of assessment of studies The characteristics of the included studies are summarized in Table 2 . Assessment was done on all 6 studies including the multiple publications from 2 studies ( 27 – 31 ). According to the Oxford Centre for Evidence-Based Medicine levels of evidence ( 25 ), 3 articles ( 32 , 33 , 36 ) were classified as 1b, indicating high-quality individual RCTs, while 8 ( 27 – 31 , 34 , 35 , 37 ) was classified as 2b, indicating articles with less than 80% follow-up, participant retention, or participant data analyzed. View this table: View inline View popup Download powerpoint Table 2: Risk-of-Bias Analysis of Studies Examining Dietary Interventions and Gut Microbiota in Individuals at Risk for Colorectal Cancer Using the Academy of Nutrition and Dietetics Quality Criteria Checklist for Primary Research risk-of-bias assessment ( 26 ), 3 articles ( 32 , 33 , 36 ) were rated positively (III), reflecting adequate consideration of bias, participant retention, and robustness, validity, and rigor in the intervention design, data collection, and analysis. Seven studies ( 27 – 31 , 34 , 35 ) were rated neutral (Ø), indicating they were neither exceptionally robust nor notably weak. One study ( 37 ) was rated negative, primarily because it was a pilot study that did not adequately address all validity criteria. Most studies in this review exhibited statistical limitations, particularly in assessing group-by-time interactions to analyze microbiome outcomes comprehensively. Common issues included reliance on within-group changes without assessing intergroup differences over time in the same statistical model. Other issues pertained to lack of clarity in P-value reporting, and inconsistent application of false discovery rate (FDR) corrections for taxa-level analyses. Discussion Summary of evidence This systematic review highlights the limited availability of RCT dietary interventions focusing on microbiome outcomes in individuals at risk for CRC. Existing evidence largely stems from observational studies examining associations between microbiome profiles and CRC risk, or diet and CRC risk ( 6 , 13 – 22 ). Additionally, many dietary intervention trials that were excluded focused on pre- or post-operative microbiome outcomes, recovery from surgery, or dietary effects combined with drugs or other therapies. Such studies, while valuable, do not provide direct insights into the potential of dietary interventions for modulating the microbiome for CRC prevention or recurrence in at-risk populations. In this context, the findings of this review shed light on how specific dietary interventions influence microbial diversity, taxonomic composition, and microbial-produced metabolites in CRC-risk individuals. Microbial diversity and taxonomic composition shifts Functional compound interventions, such as ginger extract supplementation, showed no significant changes in stool diversity metrics, however, reductions in Bacteroides , Akkermansia , and Ruminococcus compared to placebo were observed ( 33 ). While Bacteroides includes both beneficial and pathogenic species ( 39 ), the reduction in Akkermansia , a bacterium involved in mucin degradation and gut barrier maintenance ( 40 , 41 ), and Ruminococcus , a genus of complex carbohydrate degraders ( 42 ), raises questions about whether these microbial shifts reflect unintended effects of ginger supplementation itself or are influenced by concurrent dietary changes during the intervention. The β-glucan-enriched bread intervention ( 37 ), derived from barley, demonstrated a fluctuating pattern regarding the abundance of pathogenic taxa Clostridium perfringens ( 43 ), and a decrease in total coliforms which seems to align with previous literature on oat or barley derived beta glucan ( 44 , 45 ), and could be a result of the limited ability of coliform strains to metabolize b-glucan ( 37 ). Rice bran interventions ( 35 , 36 ) exhibited variable effects, with a 4-wk study reporting increased microbial diversity ( 35 ), while a 24-wk study showed no significant changes ( 36 ). Over 2 wk, rice bran increased abundance of Bacteroidetes and reduced the Firmicutes-to-Bacteroidetes ratio ( 35 ), which may reflect enhanced carbohydrate fermentation ( 46 ) and increased production of metabolites like propionate, known for their anti-inflammatory effects ( 47 ). Over 24 wk, rice bran was associated with an increase in Lactobacillus and Firmicutes compared to rice powder ( 36 ). Lactobacillus , consistent with rice bran’s prebiotic potential, contributes to gut homeostasis by reinforcing gut barrier integrity and preventing intestinal damage from microbial pathogens ( 48 ). However, the broader increase in Firmicutes remains challenging to interpret, as this phylum includes both beneficial and pro-inflammatory species ( 49 , 50 ), highlighting the importance of finer taxonomic and functional analyses. Whole food interventions such as navy beans ( 32 ), demonstrated notable increases in microbial diversity, with significant improvements in alpha-diversity indices observed over 4 to 8 wk. Navy bean consumption enriched beneficial taxa like Faecalibacterium and Bifidobacterium ( 51 – 53 ), known for their anti-inflammatory properties and butyrate production ( 53 – 55 ), both critical in CRC prevention ( 56 , 57 ). Concurrently, pro-inflammatory taxa like Collinsella and Ruminococcus torques ( 58 , 59 ) decreased, further supporting the protective role of navy beans in modulating the gut microbiome. However, these effects were not replicated in an intervention using navy bean powder ( 35 ), which showed no significant taxonomic changes over a shorter duration. Processing, such as grinding beans into powder, may alter nutrient bioavailability ( 60 , 61 ) and microbial utilization patterns, but its impact on taxonomic composition remains unclear, emphasizing the need for comparative studies to evaluate the influence of food processing and baseline microbiota. Dietary patterns such as Healthy Eating and Mediterranean diets ( 34 ) did not significantly alter taxonomic composition in colon mucosal microbiota, likely reflecting the inherent stability of the mucosal microbiota compared to the more dynamic stool microbiota. However, a reduction in θYC community dissimilarity within the Healthy Eating group over 26 wk suggests increased uniformity in microbial communities among participants. This stability in taxonomic composition may still coincide with functional or metabolite changes, highlighting potential shifts in microbial activity that contribute to CRC risk modulation. Building on these structural changes in the microbiome, an examination of microbial metabolites shifts provides critical insights into the functional impacts of dietary interventions and their relevance to CRC prevention Microbial produced metabolites Functional food interventions, such as rice bran and β-glucan-enriched bread, modulated microbial metabolism in ways potentially protective against CRC. Rice bran increased beneficial SCFAs including acetate and propionate ( 35 ), while simultaneously reducing harmful microbial byproducts such as glycodeoxycholate and p-cresol sulfate ( 31 ), suggesting suppression of tumor-promoting pathways ( 31 , 62 – 64 , 17 , 65 , 66 ). Similarly, β-glucan-enriched bread increased acetate levels ( 37 ), reinforcing the role of SCFAs in supporting epithelial repair, reducing inflammation, and maintaining gut integrity ( 67 ), key processes in CRC prevention ( 68 , 69 ). Navy bean and rice bran interventions increased several polyphenol-derived metabolites, including enterolactone ( 29 ) and diosmin ( 31 ), both of which exhibit antioxidant and anti-inflammatory properties ( 70 – 77 ). Navy bean consumption also elevated salicylate ( 29 ), a plant-derived phenolic compound ( 78 ), known for its anti-inflammatory and anti-cancer effects ( 79 ). Notably, salicylate has been shown at pharmacological concentrations in vitro to reduce angiogenesis triggered by colon cancer cells ( 80 ). Another metabolite elevated by navy beans was 3-(4-hydroxyphenyl)propionate, a proanthocyanidin derivative ( 81 ) with demonstrated antioxidant and anti-inflammatory potential ( 82 , 83 ). In addition to phenol metabolites, navy bean interventions increased amino acid-related metabolites such as pipecolic acid ( 32 ) and cadaverine ( 29 ) which are associated with anti-inflammatory, anti-tumor effects ( 32 , 84 , 30 ), and gut barrier function ( 85 , 86 ). Interestingly, cadaverine is also elevated in CRC patients ( 87 ), suggesting its dual role as both a physiological modulator and a potential biomarker. Navy beans also increased plant alkaloid trigonelline ( 88 , 89 ), which has been linked to reduced CRC risk through suppression of Nrf2 activation, potentially limiting oxidative stress and tumor progression ( 90 , 91 ). Dietary patterns, such as Healthy Eating and Mediterranean diets, shifted metabolic outputs away from proteolytic fermentation pathways, reducing metabolites like total branched-chain bacterial fatty acids. While some studies suggest a general protective effect of BCFAs on gut inflammation ( 92 , 93 ), elevated levels of fecal BCFAs have been found in CRC cohorts likely due to microbial fermentation of host-derived substrates such as sloughed intestinal cells ( 94 ). This shift in response to these dietary interventions may indicate less fermentation of host-derived substrates. Figure 2 illustrates the effects of dietary interventions on microbial-derived metabolite changes with Supplementary Table S3 providing additional context regarding the metabolite role and potential implications for CRC risk. Download figure Open in new tab Figure 2: Sankey diagram illustrating the effects of dietary interventions on microbial-derived metabolite changes in individuals at risk for CRC. The first column categorizes metabolites based on their biochemical or functional classification and each class is assigned a unique color to indicate metabolite type. The second column lists dietary interventions, and the color serves as a linking factor between interventions and metabolites. The third column displays the specific microbial metabolites affected by these interventions and the color corresponds to the observed effect in response to the intervention, as indicated in the last column: green denotes increased metabolite levels, while purple indicates decreased levels. The observed metabolite shifts underscore the microbiome’s critical role as a mediator between diet and host physiology, influencing colorectal cancer (CRC) risk. Microbial mechanisms contributing to CRC risk include the “driver-passenger" model theory which suggests that "driver" bacteria, initiate CRC by causing DNA damage, leading to the accumulation of mutations in colonic epithelial cells ( 15 , 95 ). These driver bacteria are eventually replaced by "passenger" bacteria, which thrive in the altered tumor microenvironment both promoting inflammation through activation of pathways such as NF-κB and Wnt/β-catenin, and tumor progression ( 15 , 95 ). Another theory purports that microbial dysbiosis can alter host energy metabolism by shifting from oxidative phosphorylation to glycolysis (Warburg effect), downregulating the tricarboxylic acid (TCA) cycle, and enhancing lactate production— hallmarks of cancer metabolism ( 21 ). Dietary interventions can modulate these microbiome-mediated pathways to reduce CRC risk. In vitro studies demonstrate that SCFAs can inhibit histone deacetylase (HDAC) activity, supporting tumor suppression pathways ( 96 , 97 ). Resistant starch, which is readily metabolized by bacteria, and found in many foods, can influence CRC-related DNA methylation patterns, enhancing genome stability (e.g., GADD45A upregulation), decreasing cell cycle proliferation (e.g., CDK4 expression), and lowering crypt mitotic cell proportion in the upper half of the crypt in tumor tissue ( 13 , 98 ), contributing to anti-neoplastic effects. Additionally, polyphenols, either directly or through their microbial degradation byproducts, can reduce CRC risk by exerting antioxidant and anti-inflammatory effects, modulating signaling pathways, enhancing gut barrier integrity, promoting butyrate production, exhibiting selective antibacterial activity, and suppressing carcinogenic bacteria ( 22 ). This highlights the potential of dietary interventions to modulate these complex microbiome-mediated mechanisms. Strengths and Limitations This systematic review’s strength lies in its exclusive focus on RCTs, providing a stronger quality of evidence on how dietary interventions influence the gut microbiome in individuals at risk for CRC. Many existing studies on diet and CRC have been prospective cohort or case-control studies, particularly in the context of microbiome outcomes. While such studies have been included in previous reviews ( 6 , 13 – 22 ), this review emphasizes the scarcity of RCTs, underscoring the need for more controlled studies, particularly in diverse populations. The review also provides a detailed analysis of microbial-derived metabolites, offering mechanistic insights into how dietary interventions may modulate CRC risk through metabolic pathways. One limitation of this review is the inability to explore differences across the lifespan. Age-related changes in the gut microbiome such as reduced diversity and differences in taxonomic composition and functional capacity ( 99 , 100 ), may influence how individuals respond to dietary interventions. While older adults have traditionally been at higher CRC risk, recent trends indicate a plateau in this age group, whereas early-onset CRC is rising among younger adults (<50 years) ( 101 , 102 ). Moreover, while nutrient-rich dietary patterns consumed earlier in life may attenuate this risk ( 103 , 104 ), the lack of age-stratified RCTs prevented an in-depth analysis of these potentially critical life-stage differences. Additionally, the studies included in this review primarily focused on short- to medium-term dietary interventions, limiting insights into the long-term effects of diet on the microbiome and CRC risk. This constraint underscores the need for cautious interpretation of the findings. Implications of findings, research gaps, and future directions A critical gap highlighted by this systematic review is the limited availability of RCTs examining the effects of dietary interventions on gut microbiome outcomes in individuals at risk for CRC. While the reviewed studies provide valuable insights into how specific foods and dietary patterns influence microbial diversity, taxonomic composition, and metabolite production, translating these findings into clinical strategies for CRC prevention requires deeper mechanistic insights. Future studies should move beyond cataloging microbial composition and focus on how microbial functions influence CRC-related pathways, including inflammation, epithelial integrity, and immune modulation. One notable research gap is the underutilization of advanced methodologies to assess the functional capacity of the microbiome ( 105 ). While many studies have measured common microbial metabolites such as SCFAs and secondary bile acids, few have employed advanced functional analyses, meta-transcriptomics or metabolomics, to elucidate other microbial pathways involved in amino acid metabolism, polyphenol biotransformation, generation of bioactive metabolites, and their influence on host metabolic and immune responses. Incorporating these analyses into future dietary interventions could uncover novel mechanisms by which diet influences CRC risk through microbiome modulation The findings also underscore the need to examine whole dietary patterns rather than isolated food components. Much of the existing literature focuses on selected dietary components, such as fiber, without considering the broader dietary context that accounts for macronutrient and micronutrient interactions. For example, while fiber is widely recognized for its gut health benefits ( 106 , 107 ), its effects on the microbiome may be more pronounced within nutrient-rich diets that contain other healthy dietary components such as polyphenols ( 108 ). Similarly, fiber intake may counteract some of the adverse effects of high red meat consumption on CRC risk and inflammatory outcomes ( 20 , 109 ). It may not be individual dietary components alone but rather the overall dietary pattern that influences CRC risk. However, this nuanced approach, considering dietary patterns in RCTs to assess microbiome-related outcomes, remains underexplored in CRC prevention research. Additionally, most studies have predominantly focused on Western diets and populations limiting the broader applicability of the findings. Expanding RCT research to include dietary patterns predominant in regions with low CRC risk such as Southern Asia, or Sub-Saharan Africa ( 110 ), could provide valuable insights into protective dietary components and distinct diet-microbiome interactions. This approach could help identify how interaction of specific dietary elements (e.g., high fiber content, unique polyphenols, or fermented foods) contribute to the reduced CRC risk observed in these populations. To advance this field, future studies should prioritize robust trial designs with clear endpoints that also extend beyond microbiome outcomes to include host-relevant outcomes, such as gut barrier function, systemic inflammation, and specific cancer-related pathways. Utilizing insights from epidemiological and computational studies that highlight CRC-associated metabolic signatures could guide the testing of dietary interventions associated with specific microbial or metabolic pathways. Addressing these research gaps could pave the way for microbiome-targeted dietary strategies that are not only preventive but also tailored to individual risk profiles, potentially transforming CRC prevention and management strategies. Conclusion This systematic review emphasizes the significant yet underexplored potential of dietary interventions to modulate gut microbiome pathways relevant to CRC prevention. While current evidence demonstrates that whole foods, nutrient-rich dietary patterns, and specific functional compounds can positively influence microbial diversity, composition, and metabolite production, substantial research gaps remain. Future studies must prioritize RCTs on dietary patterns in diverse populations and integrate advanced microbiome functional analyses to better delineate the diet-microbiome-CRC axis. Harnessing this knowledge could lead to tailored dietary strategies that effectively reduce CRC risk through microbiome modulation. Data Availability All data produced in the present work are contained in the manuscript. Author contributions The authors’ contributions were as follows—JD, SP, and JS: planned and developed the data collection tables; SP and JS: performed data extraction; JD: verified the accuracy of the extracted data; JD, SP, JS: conducted data analysis and prepared the manuscript; and all authors: reviewed, approved, and take full responsibility for the final manuscript. Conflict of interest The authors report no conflicts of interest. Data Sharing All data has been presented in the main manuscript and the supplementary file. Footnotes Disclaimers: No disclaimers to report. Sources of Support: This study was conducted without any external funding. The authors declare that no sponsors or funding sources were involved in the study design, data collection, analysis, interpretation, or preparation of the manuscript. 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Share A Systematic Review of the Effects of Diet on the Gut Microbiota in Individuals at Risk for Colorectal Cancer Saurabh Pandey , Jenna Skidmore , Jaapna Dhillon medRxiv 2025.04.14.25325698; doi: https://doi.org/10.1101/2025.04.14.25325698 Share This Article: Copy Citation Tools A Systematic Review of the Effects of Diet on the Gut Microbiota in Individuals at Risk for Colorectal Cancer Saurabh Pandey , Jenna Skidmore , Jaapna Dhillon medRxiv 2025.04.14.25325698; doi: https://doi.org/10.1101/2025.04.14.25325698 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Nutrition Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5422) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feab882bc0458d3',t:'MTc3OTI3MzYwMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();