Effects of Pulses Through the Gut Microbiome and Bioavailability of Bioactive Compounds (LEGUMINIBUS)

Discovering the Effects of Pulses Through the Gut Microbiome and Bioavailability of Bioactive Compounds

The goal of this clinical trial is to investigate the effects of replacing red meat with pulses, on cardiometabolic health and gut microbiome in individuals with unhealthy habits and sedentary lifestyles at high risk for cardiovascular diseases. The main questions it aims to answer are:

1. How does the substitution of red meat with pulses affect some markers of cardiovascular risk?
2. How does this dietary intervention influence the composition and function of the gut microbiome, nutritional status, well-being indices, and biomarkers related to metabolic, oxidative, inflammatory, immune, and intestinal permeability status?

Participants will:

• be assigned to either the Pulses Diet (PulD) group or the Plant Proteins Diet (PPD) group or the Habitual diet (HabD) group;
• follow their habitual diet (HabD) or the prescribed dietary plan designed on individual habitual diet to be isocaloric and isoprotein but replacing red meat with pulses (PulD group) or a combination of pulses and plant-based meat substitutes (PPD group);
• keep their physical activity levels unchanged during the entire intervention period;
• be required to complete 7-day food diaries and associated questionnaires on appetite, along with additional questionnaires related to physical activity levels, overall well-being, mood, sleep quality, stool frequency and consistency at each nutritional intervention time-point.

Researchers will compare PulD, PPD, and HabD to assess if the dietary interventions have an impact on cardiometabolic health and gut microbiome.

Study Overview

• Status: Recruiting
• Conditions: Lifestyle-related Condition
• Intervention / Treatment: Other: Pulses diet; Other: Plant proteins diet; Other: Habitual diet

Detailed Description

Legumes are recognized for their distinctive nutritional profile, rich in plant-based proteins, low-glycemic-index carbohydrates, fiber, B vitamins, minerals, and polyphenols. Due to their protein content and amino acid composition, legumes in combination with grains can effectively replace meat and its derivatives. Despite worldwide nutritional guidelines recommending legumes as the predominant source of dietary protein, the consumption of red meat and meat products remains high and may have negative consequences for public health. Indeed, epidemiological evidence indicates that long-term consumption of increasing amounts of red and processed meats is associated with a higher risk of mortality, cardiovascular diseases, colon cancer, and type 2 diabetes. Furthermore, a recent study has shown that higher intake of red meat and choline is associated with higher concentrations of trimethylamine-N-oxide (TMAO), a gut microbiota byproduct that has been associated with a higher incidence of adverse cardiovascular events. Numerous research studies indicate that the consumption of plant-based foods brings health benefits for humans and supports the recommendation of international guidelines to modify dietary habits towards a diet richer in plant-based products. In addition, epidemiological studies show a possible association between high legume consumption and a decrease in coronary heart disease and colorectal adenoma, while the evidence for a protective role of legumes against cardiovascular diseases is less strong due to heterogeneity in results and/or potential confounding factors. The ability of legumes to reduce cardiometabolic risk factors is also supported by various scientific evidence from clinical trials. These studies demonstrate that legume consumption has a positive effect on lipid profile, glucose metabolism, blood pressure, body weight, oxidative stress, and inflammatory status.

Despite the recognized health benefits of consuming legumes regularly, there is still a limited understanding of the underlying physiological mechanisms that drive these positive effects. An observational study conducted in Italy shed some light on this issue by demonstrating that individuals who closely adhere to the Mediterranean diet, which emphasizes reducing red meat consumption and increasing the intake of fruits, vegetables, and legumes, have gut microbiota characterized by a higher abundance of fiber-degrading bacteria. These individuals also exhibit higher levels of short-chain fatty acids in their feces and lower concentrations of TMAO in their urine. Furthermore, a randomized controlled trial conducted on individuals at risk of cardiovascular diseases due to an unhealthy lifestyle revealed that shifting from a typical Western diet to a more Mediterranean-like pattern led to an increase in the presence of fiber-degrading bacterial species in the gut microbiota. This dietary change also resulted in elevated circulating microbial metabolites associated with improved inflammatory status. While there is still a lack of comprehensive in vivo studies assessing the bioavailability of nutrients from legumes, a few clinical trials have investigated the influence of legumes on the intestinal microbiome. Nevertheless, the available literature indicates that legumes have the ability to influence the human microbiota. However, it is important to note that the specific effects of legumes on the microbiota can vary significantly across different studies, making it difficult to generalize these findings to all types of legumes.

In this framework, the present project will focus on the evaluation of the effect of replacing red meat with pulses (PulD) or a combination of pulses and plant-based meat substitutes (PPD) on the cardiometabolic health of individuals with unhealthy habits and sedentary lifestyles via the modification of intestinal microbial communities. Additionally, it seeks to investigate the effects on health outcomes, with a primary focus on evaluating changes in inflammatory, oxidative, immune, and hormonal status. The study will include the establishment of a 2-month dietary intervention with an isocaloric and isoprotein pulses diet (PulD) and a plant proteins diet (PPD). Coupled with detailed host phenotyping and gut microbiota profiling during and after the intervention, this will allow assessment of the causal effects of a diet rich in plant-based proteins (mainly from pulses) and the gut microbiome in populations at high risk for cardiovascular disease (CVD).

The potential eligibility of subjects to participate in this study will be assessed through pre-recruitment questionnaires. These questionnaires will collect personal and socio-demographic data of volunteers, general health information (including anthropometry, health status, medical history, smoking and alcohol consumption habits), details about individual dietary habits using the Food Frequency Questionnaire (FFQ), information about eating behavior through the Three Factor Eating Questionnaire (TFEQ), and levels of physical activity using the International Physical Activity Questionnaire (IPAQ). Subjects in the PulD group and PPD group will be assigned a personalized diet prepared on the basis of own eating habits as established by 7-day food diary recalls. Energy values and whole macronutrient composition of habitual diets will be kept unchanged during PulD and PPD intervention. However, changes in carbohydrate (dietary fibre vs. starch), dietary fat (saturated vs. mono/polyunsaturated fatty acids), and protein (vegetable vs. animal) composition will be applied as a consequence of replacing meat with pulses (PulD group) or with a mix of pulses and plant-based meat substitutes (PPD group). Control subjects will not change their habitual diet (HabD) during intervention. All subjects will be requested not to change physical activity levels during the 8 weeks intervention period. Compliance will be assessed every 2 weeks with a phone interview in order to evaluate the dietary intake and physical activity during the previous week. At each intervention time-point (baseline, 4 weeks, 8 weeks), for the nutritional check, subjects will complete 7-day food diaries and associated questionnaires on appetite (Visual Analog Scale, VAS) related to the previous week before the nutritional analysis. Additionally, measurements of blood pressure, weight, circumferences (waist and hips), and body composition through bioimpedance testing will be conducted. During the intervention period, subjects will be asked to fill out the International Physical Activity Questionnaire (IPAQ), questionnaires on quality of life (QoL), on depression, anxiety and stress (DASS), the King's Stool Chart (KSC) to evaluate frequency, weight, and consistency of feces, along with the Pittsburgh Sleep Quality Index (PSQI) to evaluate the quality of sleep.

Further analysis of compliance will be conducted based on metabolomics, allowing discrimination of animal/vegetable protein intake. Metabolomes (well known to reflect both diet and microbial metabolism) will also be compared between categories in order to identify protective or risk profiles using both bioinformatics and chemometrics approaches. Metagenomes will be analyzed following Standard Operating Procedures (SOPs) utilized in landmark studies already published. Comparison of predefined groups of individuals will allow identification of microbial genes that have different abundance in the groups. Furthermore, genes will be associated with continuous variables of clinical and nutritional interest (e.g., intake of specific dietary components, insulin sensitivity) by covariance analysis. Concatenated datasets of physiological output data, metagenomic and metabolome profiles from the intervention studies will be used to predict subsets of features by multivariate analysis (PLS-DA) that can classify subjects according to their relative adherence to a PulD or PPD. The profile will be used to probe the microbiome for specific alterations as a function of the interventions.

The sample size needed to detect an effect of PulD and/or PPD on individual TMAO levels is defined based on previous study from Crimarco and colleagues. It was calculated that a sample of 28 participants per group would allow detecting a minimum difference of approximately -1.3 μM (-38%) in TMAO and approximately -0.9 mM (-18%) in cholesterol between each of the 2 test treatments vs. control and between the two test treatments, with a power of 80% and an α-error 0.017 to account for multiple comparisons (T-test).

• Study Type: Interventional
• Enrollment (Estimated): 84
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Paola Vitaglione, Professor; Phone Number: +390812539357; Email: paola.vitaglione@unina.it

Study Locations

• Italy
  • Portici, Italy, 80055
    • Recruiting
    • Department of Agricultural Sciences, Federico II University
    • Contact: Paola Vitaglione, Professor; Phone Number: +390812539357; Email: paola.vitaglione@unina.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• men and women aged 18-65;
• 20 ≤ BMI ≤ 35 kg/m2;
• habitual diet characterized by ≥ 3 medium servings of fresh red meat or processed meat (equivalent to a portion weight of 100g of fresh meat and 50g of cured meats);
• habitual diet without probiotics, functional foods, and/or any type of food supplements;
• low level of physical activity (sedentary lifestyle);
• signing the informed consent form and expressing consent for the processing of personal data.

Exclusion Criteria:

• Food allergies and intolerances, such as celiac disease, lactose intolerance, and others;
• Gastrointestinal disorders of any kind;
• Significant medical conditions;
• Pregnancy or breastfeeding;
• Hypertriglyceridemia (Triglycerides > 200 mg/dL);
• Hypercholesterolemia (Cholesterol > 200 mg/dL);
• Diabetes (Blood glucose ≥ 126 mg/dL);
• Hypertension (Blood pressure > 140/90 mm Hg);
• Weight loss ≥ 3 kg in the past 2 months prior to the study;
• Use of any medication at enrollment and in the 2 months prior to the study;
• Regular diet rich in fruits and vegetables;
• Consumption of alcohol equivalent to or exceeding 3 glasses of wine per day;
• Concurrent participation in other clinical trials.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Pulses Diet (PulD); Subjects will follow a Pulses-enriched diet for 2 months.	Other: Pulses diet; Subjects in the PulD group will be assigned a personalized diet prepared based on their own eating habits as established by 7-day food diary recalls. Energy values of habitual diets will be kept unchanged during the PulD intervention. The diet will be characterized by isocaloric and isoprotein substitutions, replacing habitual servings of red meat or processed meat with servings of pulses.
Experimental: Plant Proteins Diet (PPD); Subjects will follow a Plant protein-enriched diet for 2 months.	Other: Plant proteins diet; Subjects in the PPD group will be assigned a personalized diet prepared based on their own eating habits as established by 7-day food diary recalls. Energy values of habitual diets will be kept unchanged during the PPD intervention. The diet will be characterized by isocaloric and isoprotein substitutions, replacing habitual servings of red meat or processed meat with a mixture of pulses and plant-based meat substitutes.
Active Comparator: Habitual Diet (HabD); Subjects will follow a habitual diet for two months.	Other: Habitual diet; Control subjects will not change their habitual diet during intervention. All subjects will be requested not to change physical activity levels during the 8 week intervention period.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Changes in fasting total cholesterol concentration	Measure of serum total cholesterol concentration (mg/dL serum)	2 months
Changes in plasma TMAO concentration	Measure of plasma TMAO concentration (μmol/L plasma)	2 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Changes in faecal microbiome	Measure of faecal microbiome	2 months
Changes in urinary TMAO concentration	Measure of urinary TMAO concentration (mmol/mol creatinine)	2 months
Changes in urinary polyphenols concentration	Measure of urinary polyphenols concentration (ng/mg creatinine)	2 months
Changes in urinary urolithin concentration	Measure of urinary urolithin concentration (ng/mg creatinine)	2 months
Changes in urinary betaine concentration	Measure of urinary betaine concentration (mmol/mol creatinine)	2 months
Changes in urinary carnitine concentration	Measure of urinary carnitine concentration (mmol/mol creatinine)	2 months
Changes in urinary choline concentration	Measure of urinary choline concentration (mmol/mol creatinine)	2 months
Changes in urinary tryptophan betaine concentration	Measure of urinary tryptophan betaine concentration (mmol/mol creatinine)	2 months
Changes in urinary indican concentration	Measure of urinary indican concentration (mg/g creatinine)	2 months
Changes in urinary creatinine concentration	Measure of urinary concentration (mg/dL)	2 months
Variation of complete blood count	Measure of Red blood cell (RBC) count (number of cells/mm3); Hemoglobin (Hb) concentration (g/dL); Hematocrit (HCT) percentage (%); White blood cell (WBC) count (number of cells/mm3); Platelet (PLT) count (number of cells/mm3).	2 months
Variation of blood iron status biomarkers	Measure of blood concentrations (mg/dL) of iron, ferritin, total transferrin	2 months
Variation of vitamin B status	Measure of blood folic acid and vitamin B12 concentrations (ng/mL)	2 months
Variation of individual hormonal status	Measure of plasma glucagon-like peptide 1 (GLP-1), Glucose-dependent Insulinotropic Peptide (GIP), Glucagon, Leptin, Ghrelin, C-peptide concentrations (pg/mL plasma)	2 months
Variation of plasma endocannabinoids concentration	Measure of plasma endocannabinoids concentration (ng/mL)	2 months
Variation of plasma N-acylethanolamines concentration	Measure of plasma N-acylethanolamines concentration (ng/mL)	2 months
Changes in plasma betaine concentration	Measure of plasma betaine concentration (μmol/L plasma)	2 months
Changes in plasma carnitine concentration	Measure of plasma carnitine concentration (μmol/L plasma)	2 months
Changes in plasma choline concentration	Measure of plasma choline concentration (μmol/L plasma)	2 months
Changes in plasma bioactive peptides concentration	Measure of plasma bioactive peptides concentration (ng/mL plasma)	2 months
Changes in plasma bile acids concentration	Measure of plasma bile acids concentration (ng/mL plasma)	2 months
Variation of plasma oxidative stress biomarkers	Measure in plasma of: TBARS concentration (µM); nitrotyrosine (N-Tyr) concentration (OD/mL); 8-hydroxy-2-deoxyguanosine (8-OHdG) concentration (ng/mL)	2 months
Variation of plasma antioxidant enzyme activities	Measure of plasma superoxide dismutase (SOD) activity (U/mL); catalase activity (nmol/min/mL); glutathione peroxidase (GPx) activity (nmol/min/mL)	2 months
Variation of serum dipeptidyl peptidase-IV (DPP-IV) concentration and activity	Measure of serum dipeptidyl peptidase-IV (DPP-IV) concentration (ng/mL) and activity (IU/L)	2 months
Variation of serum Triglycerides concentration	Measure of serum triglycerides concentration (mg/dL serum)	2 months
Variation of serum LDL- and HDL-cholesterol concentrations	Measure of serum LDL-, HDL-cholesterol concentrations (mg/dL serum)	2 months
Variation of serum glucose concentration	Measure of serum glucose concentration (mg/dL)	2 months
Variation of serum insulin concentration	Measure of serum insulin concentration (μU/mL serum)	2 months
Variation of serum Insulin-like Growth Factor-1 (IGF-1) concentration	Measure of serum IGF-1 concentration (ng/mL)	2 months
Variation of serum C-reactive protein (CRP) concentration	Measure of serum CRP concentration (mg/L)	2 months
Variation of serum zonulin concentration	Measure of serum zonulin concentration (ng/mL)	2 months
Changes in immune state blood markers	Measure of monocyte polarization (Arbitrary Units) of Cluster of Differentiation (CD) 86, Tumor Necrosis Factor alpha (TNFα), inducible Nitric Oxide Synthase (iNOS), CD36, CD11c, CD169, CD206, CD163, CD68, CD11b, CD16, e CD14	2 months
Variation of erythrocytes antioxidant enzymes activity	Measure in erythrocytes of: superoxide dismutase (SOD) activity (U/mL); catalase activity (nmol/min/mL); glutathione peroxidase (GPx) activity (nmol/min/mL); glutathione reductase (GR) activity (U/mL)	2 months
Changes in body weight	Measure of body weight (kg) in fasting subjects	2 months
Changes in body mass index	Calculation of body mass index (kg/m2) by using the formula weight in kilograms divided by height in meters squared.	2 months
Changes in waist and hip circumferences	Measure of waist circumference (cm) at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest. Measure of hip circumference (cm) around the widest portion of the buttocks.	2 months
Changes in blood pressure	Measure of systolic pressure and diastolic pressure in millimetres of mercury (mmHg) by using a digital sphygmomanometer	2 months
Changes in body composition	Body composition (kg body fat mass, body fat-free mass and total body water) is determined by conventional bioelectrical impedance analysis with a single-frequency 50 kilohertz (kHz) bioelectrical impedance analyzer in the postabsorptive state (fasting subjects) and after being in the supine position for 20 min. Body composition data will be calculated from bioelectrical measurements and anthropometric data by using validated predictive equations.	2 months
Variation of hunger, fullness and satiety sensation scores	Measures of hunger, fullness and satiety sensations over the day reported by subjects by using hunger Visual Analogue Scales (VAS) 0-10 centimeters. Changes in these scores may reflect potential effects of dietary intervention in modulating hunger, fullness and satiety.	2 months
Changes in stool weight, consistency and frequency	Measure of stool weight, frequency and consistency by mean of King's Stool Chart (KSC) filled out by subjects. The chart comprises three categories of stool weight : <100 g, 100-200 g, >200 g. The chart comprises four categories of stool consistency: hard and formed, soft and formed, loose and unformed, liquid. Fecal frequency is incorporated by recording the code of each feces passed over a 24 hour period.	2 months
Assessment of diet composition	Measure of individual's usual food consumption through Food Frequency Questionnaires (FFQ) by querying the frequency (times/week) and amount (g) at which the respondent consumed food items based on a predefined food list.	2 months
Changes in physical activity level	Measurement of an individual's physical activity level (MET min/week) through the International Physical Activity Questionnaire (IPAQ), which assesses the frequency (times/week) and duration (min) of different activities such as walking, moderate-intensity exercises, vigorous-intensity exercises, and sitting time.	2 months
Variation of wellbeing status	Estimate of wellbeing status by mean of quality of life (QoL) questionnaire, which is based on Short Form-12 Health Survey (SF-12), a self-report form of subjective health. Physical and Mental Health Composite Scores (PCS & MCS, arbitrary units) are computed using the scores of twelve questions and range from 0 to 100, where a zero score indicates the lowest level of health measured by the scales and 100 indicates the highest level of health.	2 months

Collaborators and Investigators

• Sponsor: Paola Vitaglione
• Investigators: Study Director: Paola Vitaglione, Professor, Department of Agricultural Sciences, Federico II University

Publications and helpful links

General Publications

• Crimarco A, Springfield S, Petlura C, Streaty T, Cunanan K, Lee J, Fielding-Singh P, Carter MM, Topf MA, Wastyk HC, Sonnenburg ED, Sonnenburg JL, Gardner CD. A randomized crossover trial on the effect of plant-based compared with animal-based meat on trimethylamine-N-oxide and cardiovascular disease risk factors in generally healthy adults: Study With Appetizing Plantfood-Meat Eating Alternative Trial (SWAP-MEAT). Am J Clin Nutr. 2020 Nov 11;112(5):1188-1199. doi: 10.1093/ajcn/nqaa203.
• Meslier V, Laiola M, Roager HM, De Filippis F, Roume H, Quinquis B, Giacco R, Mennella I, Ferracane R, Pons N, Pasolli E, Rivellese A, Dragsted LO, Vitaglione P, Ehrlich SD, Ercolini D. Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake. Gut. 2020 Jul;69(7):1258-1268. doi: 10.1136/gutjnl-2019-320438. Epub 2020 Feb 19.

Study record dates

Study Major Dates

• Study Start (Actual): January 10, 2024
• Primary Completion (Estimated): October 16, 2024
• Study Completion (Estimated): October 16, 2024

Study Registration Dates

• First Submitted: August 13, 2023
• First Submitted That Met QC Criteria: August 13, 2023
• First Posted (Actual): August 21, 2023

Study Record Updates

• Last Update Posted (Actual): January 12, 2024
• Last Update Submitted That Met QC Criteria: January 10, 2024
• Last Verified: January 1, 2024

More Information

Terms related to this study

• Keywords: Obesity; Overweight; Cardiovascular diseases; Gut microbiome; Pulses; Legumes; Plant proteins; Red meat substitution; Protein transition
• Other Study ID Numbers: DE LEGUMINIBUS

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: All individual participant data that underlie the results reported in the publication will be shared after deidentification
• IPD Sharing Time Frame: Data will become available immediately after the publications of the results with no end date.

IPD Sharing Access Criteria

Metagenomic reads generated in this study will be available (without conditions of reuse) at the European Nucleotide Archive (ENA) in European Bioinformatics Institute (EBI), by using a specific accession number.

The clinical data will be shared with any researcher who will ask the corresponding author by providing a methodologically sound proposal.

• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE

Drug and device information, study documents

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