Dietary Fiber Effects on the Microbiome and Satiety (FEMS)

Mechanisms Linking Dietary Fiber, the Microbiome and Satiety

Strong evidence supports the association between high fiber (HiFi) diets (e.g. legumes, nuts, vegetables) and a reduced risk for chronic conditions such as cardiovascular disease (CVD), type 2 diabetes and some forms of cancer. However, the current U.S. average consumption of dietary fiber of 17g/day is significantly below the recommendation level of 25g/d for women and 38g/d for men. Furthermore, fiber fermentation to produce short chain fatty acid (SCFA) products and alterations in microbial composition and activity may be mechanisms linking a HiFi diet to improved health. Importantly, much of the data, including findings supporting a beneficial role of SCFA have been derived from animal studies. Human studies are now needed to advance the understanding of the translational significance of rodent studies and the potential benefit of fiber on microbial metabolites and cardiometabolic health, glucose regulation, appetite and satiety. The central hypothesis is that that the mechanisms by which dietary fiber provides metabolic benefit include direct physical effects in the upper gastrointestinal tract to slow nutrient absorption, and indirect effects to reduce food intake mediated by SCFA-induced secretion of intestinal hormones resulting in increased satiety. Design: Using fiber derived from peas, Aim 1 will test the effect of a HiFi diet on appetite, satiety, and cardiometabolic health and whether elevated SCFA concentration mediates improved satiety in 44 overweight/obese subjects randomly assigned to receive either a high fiber or a low fiber dietary intervention for four weeks in a parallel arm-repeated measures design. Aim 2 will quantitate the changes in microbial composition and colonic SCFA production rate during HiFi feeding and whether any changes are potential mediators of observed benefits on satiety and cardiometabolic risk factors in 26 subjects assigned to receive a high fiber intervention for 3 weeks in a repeated measures design. Relevance: These studies will significantly expand the understanding of mechanisms by which dietary fiber improves satiety and cardiometabolic health in humans.

Study Overview

• Status: Recruiting
• Conditions: Dietary Fiber
• Intervention / Treatment: Other: Dietary fiber: 10-25g; Other: Dietary fiber: 5g

Detailed Description

Dietary patterns with high fiber content are linked to a lower risk for the development of cardiovascular disease [1-3], hypertension [4], type 2 diabetes [5] and increased body weight [4]. Potential biological mechanisms that may mediate these beneficial health effects include a slowing of the absorption of meal carbohydrate (CHO) [6-11], reduction in blood lipids [8,12] and an increase in the release of satiety hormones [10,13]. The PI has previously shown that compared to low-fiber (LowFi) meals, high-fiber (HiFi) meals reduced blood glucose concentrations postprandially by 11% [14]. Another potential mechanism is a postulated role for microbial fiber fermentation to improve health through production of the short chain fatty acids (SCFA) acetate, propionate, and butyrate [15-17]. In addition to promoting colon health, butyrate production may stimulate release of the gut hormones, glucagon-like peptide-1 GLP-1 and peptide YY (PYY) [18] resulting in improved appetite regulation [19]. Since the seminal paper of Gordon in 2004 [20], a large body of research has uncovered the critical role that gut microbes play in health. Importantly, much of these data, including findings supporting a beneficial role of SCFA [21-23] have been derived from animal studies. Human studies are now needed to advance the translational significance of rodent studies and the potential benefit of fiber on microbial metabolites and cardiometabolic health, glucose regulation, appetite and satiety. The current study will determine the effects of dietary fiber intake on appetite, intestinal metabolism, and the microbiome. We hypothesize that the mechanisms by which dietary fiber provides metabolic benefit include direct physical effects in the upper gastrointestinal (GI) tract to slow nutrient absorption and indirect effects to reduce food intake mediated by SCFA-induced secretion of GI hormones resulting in increased satiety. To test this hypothesis, we will conduct a randomized controlled trial of 4 weeks of HiFi or LowFi diets in 44 subjects (specific aim 1, SA1) and also leverage a screening colonoscopy to standardize baseline microbial populations for a 3-week, pre- and post HiFi intervention study in 26 subjects (SA2) with metabolic syndrome. We will assess the effects of the diets on appetite and satiety, cardiometabolic risk and intestinal metabolism at the beginning and at the end of the feeding interventions. The fiber chosen is derived from peas, as recent data suggest that legumes significantly improve glycemia [6,24-27], diabetes [28,29], heart disease risk [30], and risk for obesity [31]. These methods will be employed to accomplish two specific aims.

SA1a: Test the effect of a HiFi diet on appetite and satiety and whether SCFA production mediates improved satiety in HiFi feeding. Hypothesis (H) 1a: In adult men and women, the HiFi (n=22) compared to the LowFi (n=22) diet will significantly improve markers of satiety (GLP-1, PYY, subjective appetite ratings) and lower activation in brain regions that control food intake/reward/appetite while increasing activation in executive control regions during functional magnetic resonance imaging (fMRI) visual food cues. These changes will be related to higher postprandial SCFA concentrations and altered microbial populations as evidenced by greater bifidobacteria levels and low Firmicutes to Bacteroidetes ratio.

SA1b: Determine whether a HiFi diet improves cardiometabolic health. H1b: A HiFi diet will result in lower glycemia, blood lipids, blood pressure, and waist circumference compared to a LowFi diet.

SA2: Quantitate the changes in microbial composition and colonic SCFA production rate (using stable isotopic infusion techniques) on HiFi diet feeding (n=26) and whether any changes are potential mediators of observed benefits on satiety and cardiometabolic risk factors. H2: A significant microbial species reduction will follow colonoscopy bowel prep, and repopulation after HiFi will be characterized by greater bifidobacterial and low Firmicutes/Bacteroidetes ratio. An increase in SCFA flux following HiFi will be associated with improvements in microbial composition and postprandial markers of satiety and blood triglycerides and glucose excursions.

Sample size Based on our own published [14] and unpublished data, and that from others [32-35], a power analysis revealed that a sample size of between 10 to 20 subjects/group is needed to detect significant differences in key variables (alpha 0.05) and a power of 90% (15 to 18 subjects/group with 80% power). For specific aim 1, we will add 2 subjects/group to account for a 10% subject dropout and for specific aim 2, we will add an additional 6 subjects to account for 30% dropout. Thus, for specific aim 1 44 subjects (22/group) and for specific aim 2, 26 subjects are analyzed in a repeated-measures design. We believe any dietary fiber effect smaller than past, published treatments will be balanced by the relative 'clean' starting point of the colon after colonoscopy (specific aim 2) and also by the fact that we are providing all study meals and hence fully controlling the subject's intake

Data analysis:

Statistical analysis will be performed with SPSS software (version 25). Graphical methods are used to assess the appropriateness of assuming linear relationships and histograms and probability plots used to assess the normality of residuals. Transformation or non-parametric methods will be, employed as needed. Fasting glucose and hormones concentrations will be, obtained serially - both acutely after meals and in the fasting state before and after the diets. Changes over time (treated as a nominal factor so as not to assume a linear trend) and by diet in the composition of the microbiome will be assessed by grouping into the dominant bacterial phyla (i.e. Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria and Tenericutes) and genus. For SA1, a two-factor ANOVA will be used for each outcome, with the factors being Group, Time and the Group by Time interaction. The groups are constructed via matched-sample randomization, so we expect comparability at baseline. For SA2, a paired sample t-test will be used to compare outcomes of interest. Results will be reported as group means or medians, as most appropriate for the data along with 95% confidence intervals for the summary statistics. Analyses of the fMRI data during visual stimulation are performed using Statistical Parametric Mapping 12 software (www.fil.ion.ucl.ac.uk/spm). Data are preprocessed, beginning with slice timing and realignment of the images to the mean image. The anatomical T1-weighted image is co-registered to the mean functional image. Normalization into Montreal Neurological Institute (MNI) space and Gaussian spatial smoothing is then performed. For each participant (first-level analyses), a general linear model is applied for the high- and low-caloric food and non-food image conditions. For each condition, a separate regressor is modeled by using a canonical hemodynamic response function that includes time derivatives. Movement parameters are, modeled as confounders. For second level analysis, a mixed model ANOVA is used, with the within-factor, image condition (high calorie food, low calorie food, non-food|) and the between-factor group (HiFi vs LowFi). A priori regions-of-interest (ROIs) such as, insula, orbitofrontal cortex, amygdala, and prefrontal cortex are examined for potential group-by-food image interactions (the effect of most interest). Whole-brain analyses are also conducted (corrected for multiple comparisons) to identify other potential ROIs.

• Study Type: Interventional
• Enrollment (Anticipated): 88
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Katherene OB Anguah, PhD; Phone Number: (573)-882-8966; Email: anguahk@missouri.edu

Study Locations

• United States
  • Missouri
    • Columbia, Missouri, United States, 65212
      • Recruiting
      • University of Missouri-Columbia
      • Contact: Katherene O Anguah, Ph.D; Phone Number: 5738828966 573-882-8966; Email: anguahk@missouri.edu
      • Contact: Katherene O Anguah; Phone Number: 5738828966 5738828966; Email: anguahk@missouri.edu
      • Principal Investigator: Katherene O Anguah, Ph.D.
      • Sub-Investigator: Elizabeth J Parks, Ph.D.
      • Sub-Investigator: Shawn Christ, Ph.D.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 55 years (Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• Men and women (premenopausal only)
• Age 20-55y (Aim 1); 45-55y (Aim 2)
• BMI ≥25 or ≤35 kg/m2 (Aim 1); ≥25 or ≤40 (Aim 2)
• Weight stable (no fluctuations in body weight of greater than 4 kg in the last 3 months)
• Willing to consume a research diet
• Willing to provide blood and fecal samples

• At least one characteristic of the metabolic syndrome (but not diabetic)

  1. A large waistline: 35 inches or more for women 40 inches or more for men 2. High triglycerides: 150 mg/dL or higher 3. Low HDLc level: <50 mg/dL for women <40 mg/dL for men 4. High blood pressure ≥130/85 mmHg 5. Fasting blood sugar ≥100 mg/dL

• Pre-diabetes acceptable (glucose <125 mg/dL or HbA1c <6.5%)
• Stably treated with statin drugs, anti-hypertensives, and anti-depressants. These are acceptable as long as the drug category does not alter appetite, body weight, or the microbiome (if known)

Exclusion Criteria:

• Pregnant or lactating
• Postmenopausal (evidence suggests an interplay between the gut microbiome)
• BMI of <25 or >35 kg/m2 (Aim 1); <25 or >40 kg/m2 (Aim 2)
• Use of medications that affect the gut microbiome (e.g. antibiotics)
• Taking medications known to affect appetite (e.g., phentermine) or gastrointestinal function (e.g., metformin)
• On a special diet or undergoing weight loss, vegetarian, or other restricted dietary patterns
• Ad libitum intake of fiber above 25g/day (mean intake in the US population is 17g/day) and < 10g/d
• Ad libitum alcohol intake of greater than 1 drink/d for women and 2 drinks/d for men
• History of disease (example colon cancer, HIV, cardiovascular disease, psychiatric disorders, etc.)
• Use of tobacco products
• Having metal or implants in the body that are not MRI compatible (Aim 1 only)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: High Fiber diet; Group receiving a high fiber diet	Other: Dietary fiber: 10-25g; 10-25 g/day of fiber
Other: Low Fiber diet; Control group receiving a low fiber diet	Other: Dietary fiber: 5g; 5 g/day of fiber

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in microbiome composition and diversity	Fecal samples are collected on different days during the intervention for microbiome analyses using 16rRNA technique	Aim 1: On day 1, on 3 separate days during the intervention and on day 28 of the high fiber or low fiber intervention; Aim 2: within 14 days of scheduled colonoscopy visit and on 7 separate days during the intervention
Short chain fatty acid concentration in plasma	Plasma SCFA are analyzed using gas chromatography/mass spectrometry (GC/MS)	At the start and the final day on the intervention for both Aims 1 and 2
Short chain fatty acids enrichment	Subjects are infused with stable isotopes of the short chain fatty acids, acetate, propionate, and butyrate and then isotope dilution by an unlabeled fiber fromt he diet is used to quantify the levels of acetate, propionate and butyrate in vivo	On day 2 and day 21 of the high fiber intervention-only for Aim 2

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in blood oxygenation level dependent (BOLD) response	ubjects view images of food (low calorie and high calorie) and non-food while being scanned in an fMRI machine. The change in brain activation in response to the fMRI food reactivity task is measured as the BOLD response	Aim 1: On day 1 and day 28 of the high fiber or low fiber intervention
Subjective appetite	Subjects rate on a visual analogue scale (VAS) at 8 different time points in Aim 1 and at 12 different time points in Aim 2 during each of the two meal test visits. The VAS is a 100 mm scale to determine subjective appetite measures (hunger, fullness, desire to eat and prospective consumption).	Aim 1: On day 1 and day 28 of the high fiber or low fiber intervention. Aim 2: On day 2 and day 21 of the high fiber intervention
Glucose and lipids and blood pressure	During each of two meal test visits for Aim 1 and Aim 2, blood samples are taken at 8 different time points for Aim 1 and 12 different time points for Aim 2 for assessment of glucose response and lipids (TG) concentrations. Blood pressure measurements are taken at the beginning of each meal test day visit for both aims.	Aim 1: On day 1 and day 28 of the high fiber or low fiber intervention. Aim 2: On day 2 and day 21 of the high fiber intervention
Change in appetite hormones (GLP-1 and PYY)	Blood is drawn at different time points in both Aims 1 and 2 during each of the two meal test visits for assessment of appetite hormone	Aim 1: On day 1 and day 28 of the high fiber or low fiber intervention. Aim 2: On day 2 and day 21 of the high fiber intervention

Collaborators and Investigators

• Sponsor: University of Missouri-Columbia

Publications and helpful links

General Publications

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• Sandberg JC, Bjorck IM, Nilsson AC. Rye-Based Evening Meals Favorably Affected Glucose Regulation and Appetite Variables at the Following Breakfast; A Randomized Controlled Study in Healthy Subjects. PLoS One. 2016 Mar 18;11(3):e0151985. doi: 10.1371/journal.pone.0151985. eCollection 2016.
• de Carvalho CM, de Paula TP, Viana LV, Machado VM, de Almeida JC, Azevedo MJ. Plasma glucose and insulin responses after consumption of breakfasts with different sources of soluble fiber in type 2 diabetes patients: a randomized crossover clinical trial. Am J Clin Nutr. 2017 Nov;106(5):1238-1245. doi: 10.3945/ajcn.117.157263. Epub 2017 Aug 30.
• Jenkins DJ, Kendall CW, Popovich DG, Vidgen E, Mehling CC, Vuksan V, Ransom TP, Rao AV, Rosenberg-Zand R, Tariq N, Corey P, Jones PJ, Raeini M, Story JA, Furumoto EJ, Illingworth DR, Pappu AS, Connelly PW. Effect of a very-high-fiber vegetable, fruit, and nut diet on serum lipids and colonic function. Metabolism. 2001 Apr;50(4):494-503. doi: 10.1053/meta.2001.21037.
• Sandberg JC, Bjorck IME, Nilsson AC. Impact of rye-based evening meals on cognitive functions, mood and cardiometabolic risk factors: a randomized controlled study in healthy middle-aged subjects. Nutr J. 2018 Nov 6;17(1):102. doi: 10.1186/s12937-018-0412-4.
• Anguah KO, Wonnell BS, Campbell WW, McCabe GP, McCrory MA. A blended- rather than whole-lentil meal with or without alpha-galactosidase mildly increases healthy adults' appetite but not their glycemic response. J Nutr. 2014 Dec;144(12):1963-9. doi: 10.3945/jn.114.195545. Epub 2014 Oct 8.
• Koh A, De Vadder F, Kovatcheva-Datchary P, Backhed F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell. 2016 Jun 2;165(6):1332-1345. doi: 10.1016/j.cell.2016.05.041.
• Yadav H, Lee JH, Lloyd J, Walter P, Rane SG. Beneficial metabolic effects of a probiotic via butyrate-induced GLP-1 hormone secretion. J Biol Chem. 2013 Aug 30;288(35):25088-25097. doi: 10.1074/jbc.M113.452516. Epub 2013 Jul 8.
• Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A. 2004 Nov 2;101(44):15718-23. doi: 10.1073/pnas.0407076101. Epub 2004 Oct 25.
• Bartolomaeus H, Balogh A, Yakoub M, Homann S, Marko L, Hoges S, Tsvetkov D, Krannich A, Wundersitz S, Avery EG, Haase N, Kraker K, Hering L, Maase M, Kusche-Vihrog K, Grandoch M, Fielitz J, Kempa S, Gollasch M, Zhumadilov Z, Kozhakhmetov S, Kushugulova A, Eckardt KU, Dechend R, Rump LC, Forslund SK, Muller DN, Stegbauer J, Wilck N. Short-Chain Fatty Acid Propionate Protects From Hypertensive Cardiovascular Damage. Circulation. 2019 Mar 12;139(11):1407-1421. doi: 10.1161/CIRCULATIONAHA.118.036652.
• Ganesh BP, Nelson JW, Eskew JR, Ganesan A, Ajami NJ, Petrosino JF, Bryan RM Jr, Durgan DJ. Prebiotics, Probiotics, and Acetate Supplementation Prevent Hypertension in a Model of Obstructive Sleep Apnea. Hypertension. 2018 Nov;72(5):1141-1150. doi: 10.1161/HYPERTENSIONAHA.118.11695.
• Micioni Di Bonaventura MV, Cecchini C, Vila-Donat P, Caprioli G, Cifani C, Coman MM, Cresci A, Fiorini D, Ricciutelli M, Silvi S, Vittori S, Sagratini G. Evaluation of the hypocholesterolemic effect and prebiotic activity of a lentil (Lens culinaris Medik) extract. Mol Nutr Food Res. 2017 Nov;61(11). doi: 10.1002/mnfr.201700403. Epub 2017 Aug 29.
• McCrory MA, Hamaker BR, Lovejoy JC, Eichelsdoerfer PE. Pulse consumption, satiety, and weight management. Adv Nutr. 2010 Nov;1(1):17-30. doi: 10.3945/an.110.1006. Epub 2010 Nov 16.
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• Mayengbam S, Lambert JE, Parnell JA, Tunnicliffe JM, Nicolucci AC, Han J, Sturzenegger T, Shearer J, Mickiewicz B, Vogel HJ, Madsen KL, Reimer RA. Impact of dietary fiber supplementation on modulating microbiota-host-metabolic axes in obesity. J Nutr Biochem. 2019 Feb;64:228-236. doi: 10.1016/j.jnutbio.2018.11.003. Epub 2018 Nov 26.
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• Hashemi Z, Fouhse J, Im HS, Chan CB, Willing BP. Dietary Pea Fiber Supplementation Improves Glycemia and Induces Changes in the Composition of Gut Microbiota, Serum Short Chain Fatty Acid Profile and Expression of Mucins in Glucose Intolerant Rats. Nutrients. 2017 Nov 12;9(11):1236. doi: 10.3390/nu9111236.
• Kouris-Blazos A, Belski R. Health benefits of legumes and pulses with a focus on Australian sweet lupins. Asia Pac J Clin Nutr. 2016;25(1):1-17. doi: 10.6133/apjcn.2016.25.1.23.
• Singh B, Singh JP, Shevkani K, Singh N, Kaur A. Bioactive constituents in pulses and their health benefits. J Food Sci Technol. 2017 Mar;54(4):858-870. doi: 10.1007/s13197-016-2391-9. Epub 2016 Nov 21.

Study record dates

Study Major Dates

• Study Start (Actual): April 22, 2021
• Primary Completion (Anticipated): August 1, 2025
• Study Completion (Anticipated): August 1, 2025

Study Registration Dates

• First Submitted: October 26, 2020
• First Submitted That Met QC Criteria: October 26, 2020
• First Posted (Actual): November 2, 2020

Study Record Updates

• Last Update Posted (Estimate): May 4, 2023
• Last Update Submitted That Met QC Criteria: May 3, 2023
• Last Verified: May 1, 2023

More Information

Terms related to this study

• Keywords: functional magnetic resonance imaging; microbiome; cardiometabolic risk; satiety; dietary fiber; short chain fatty acids
• Other Study ID Numbers: 2025807

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No