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.
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
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Enrollment
84
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.
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.
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.
Department of Agricultural Sciences, Federico II University
Portici, Italy
Changes in fasting total cholesterol concentration
Measure of serum total cholesterol concentration (mg/dL serum)
Time frame: 2 months
Changes in plasma TMAO concentration
Measure of plasma TMAO concentration (μmol/L plasma)
Time frame: 2 months
Changes in faecal microbiome
Measure of faecal microbiome
Time frame: 2 months
Changes in urinary TMAO concentration
Measure of urinary TMAO concentration (mmol/mol creatinine)
Time frame: 2 months
Changes in urinary polyphenols concentration
Measure of urinary polyphenols concentration (ng/mg creatinine)
Time frame: 2 months
Changes in urinary urolithin concentration
Measure of urinary urolithin concentration (ng/mg creatinine)
Time frame: 2 months
Changes in urinary betaine concentration
Measure of urinary betaine concentration (mmol/mol creatinine)
Time frame: 2 months
Changes in urinary carnitine concentration
Measure of urinary carnitine concentration (mmol/mol creatinine)
Time frame: 2 months
Changes in urinary choline concentration
Measure of urinary choline concentration (mmol/mol creatinine)
Time frame: 2 months
Changes in urinary tryptophan betaine concentration
Measure of urinary tryptophan betaine concentration (mmol/mol creatinine)
Time frame: 2 months
Changes in urinary indican concentration
Measure of urinary indican concentration (mg/g creatinine)
Time frame: 2 months
Changes in urinary creatinine concentration
Measure of urinary concentration (mg/dL)
Time frame: 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).
Time frame: 2 months
Variation of blood iron status biomarkers
Measure of blood concentrations (mg/dL) of iron, ferritin, total transferrin
Time frame: 2 months
Variation of vitamin B status
Measure of blood folic acid and vitamin B12 concentrations (ng/mL)
Time frame: 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)
Time frame: 2 months
Variation of plasma endocannabinoids concentration
Measure of plasma endocannabinoids concentration (ng/mL)
Time frame: 2 months
Variation of plasma N-acylethanolamines concentration
Measure of plasma N-acylethanolamines concentration (ng/mL)
Time frame: 2 months
Changes in plasma betaine concentration
Measure of plasma betaine concentration (μmol/L plasma)
Time frame: 2 months
Changes in plasma carnitine concentration
Measure of plasma carnitine concentration (μmol/L plasma)
Time frame: 2 months
Changes in plasma choline concentration
Measure of plasma choline concentration (μmol/L plasma)
Time frame: 2 months
Changes in plasma bioactive peptides concentration
Measure of plasma bioactive peptides concentration (ng/mL plasma)
Time frame: 2 months
Changes in plasma bile acids concentration
Measure of plasma bile acids concentration (ng/mL plasma)
Time frame: 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)
Time frame: 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)
Time frame: 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)
Time frame: 2 months
Variation of serum Triglycerides concentration
Measure of serum triglycerides concentration (mg/dL serum)
Time frame: 2 months
Variation of serum LDL- and HDL-cholesterol concentrations
Measure of serum LDL-, HDL-cholesterol concentrations (mg/dL serum)
Time frame: 2 months
Variation of serum glucose concentration
Measure of serum glucose concentration (mg/dL)
Time frame: 2 months
Variation of serum insulin concentration
Measure of serum insulin concentration (μU/mL serum)
Time frame: 2 months
Variation of serum Insulin-like Growth Factor-1 (IGF-1) concentration
Measure of serum IGF-1 concentration (ng/mL)
Time frame: 2 months
Variation of serum C-reactive protein (CRP) concentration
Measure of serum CRP concentration (mg/L)
Time frame: 2 months
Variation of serum zonulin concentration
Measure of serum zonulin concentration (ng/mL)
Time frame: 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
Time frame: 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)
Time frame: 2 months
Changes in body weight
Measure of body weight (kg) in fasting subjects
Time frame: 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.
Time frame: 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.
Time frame: 2 months
Changes in blood pressure
Measure of systolic pressure and diastolic pressure in millimetres of mercury (mmHg) by using a digital sphygmomanometer
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 2 months
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