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

Study Overview
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Brief Summary
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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: How does the substitution of red meat with pulses affect some markers of cardiovascular risk? 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.

Detailed Description
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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, which showed that 24 participants in each treatment group would provide sufficient power (α-error 0.05, 80% power) to detect a minimum 32% change in TMAO levels. Additionally, based on the work of Meslier and collaborators, it is estimated that a sample size of 26 participants would be adequate to detect a 10% change in fasting total cholesterol using variation. Samples (feces, venous blood, and non-acidified 24-hour urine) will be collected at baseline, 4, and 8 weeks after starting the dietary intervention.

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

Conditions
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Lifestyle-related Condition

Intervention / Treatment
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* Other: Pulses diet
* Other: Plant proteins diet
* Other: Habitual diet


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Pulses Diet (PulD)<br>Subjects will follow a Pulses-enriched diet for 2 months. | Other: Pulses diet<br>* 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.<br>|
| Experimental: Plant Proteins Diet (PPD)<br>Subjects will follow a Plant protein-enriched diet for 2 months. | Other: Plant proteins diet<br>* 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.<br>|
| Active Comparator: Habitual Diet (HabD)<br>Subjects will follow a habitual diet for two months. | Other: Habitual diet<br>* 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.<br>|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Gut microbiome, Legumes, Pulses, Plant proteins, Cardiovascular diseases, Red meat substitution, Protein transition, Overweight, Obesity