Plant-Based Foods and (Poly)Phenol Supplementation in Gut Microbiota Modulation, Body Weight, and Cardiometabolic Risk

The primary objective of this study is to evaluate the impact of increased dietary polyphenol intake-either through a healthy plant-based diet (PBD) or supplementation-on body weight and cardiometabolic risk factors in adults with overweight or obesity. Additionally, the study seeks to explore the role of gut-microbiota-derived metabolites as potential mediators of these effects through metabolomic analyses.

The calculation determined a required sample size of 90 participants. To account for potential dropouts, a 10% buffer was added, resulting in a final target of 99 participants (33 per group); trial power of 0.8 and alfa error of 5%. A total of 99 participants (aged 25-45 years, BMI 25-35 kg/m²) with at least three cardiometabolic risk factors will be recruited from Health Centers supported by the Health Department of the Municipality in Temuco, Chile.

Participants will be randomly assigned (stratified by sex and age: <35y and ≥35y) in a 1:1:1 ratio to one of three groups for a 12-week intervention, with assessments conducted at baseline, week 6, and week 12:(1) a group following a healthy PBD rich in polyphenols, (2) a group following a healthy diet plus polyphenol supplementation (≈30g/day from maqui, green tea, cranberry, olive leaf and grape extracts), and (3) a control group receiving placebo and standard dietary recommendations. All groups will follow a caloric deficit (~1,200 kcal/day) for the entire 12-week study. This will be explained to each participant during the first visit.

Dietary data will be collected using three 24-hour dietary recalls at baseline and after 12 weeks, employing the SER 24 HDR software. The SER-24H software (University of Chile; Santiago, Chile) developed by CIAPEC (Center for Research in Food Environments and Prevention of Nutrition-Associated Diseases) at INTA, University of Chile, was used for dietary assessment. The Phenol-Explorer database estimates dietary polyphenol intake. It is the first comprehensive database on polyphenol content in foods. It contains more than 35,000 content values for 500 different polyphenols in over 400 foods.

Lifestyle, general health, and physical activity will be assessed through validated questionnaires. Anthropometric and body composition measurements will be taken using bioimpedance analysis. Blood pressure will be monitored with sphygmomanometers, while muscle strength will be measured via dynamometry.

Biological samples, including blood and plasma, will be collected at baseline and after 12 weeks. On each visit, 20ml of blood will be extracted from the antecubital vein and will be centrifuged and stored at -80ºC in the ultra-freezers of the School of Medicine at the Catholic University of Temuco until used.

Additionally, participants will be invited to provide a faecal sample during the first and third visits. These samples are not part of the main study but will be stored at -80ºC for potential future microbiota analyses (School of Medicine at the Catholic University of Temuco).

Biochemical analyses will be performed by institutional laboratory. Blood tests will include rutinary hematology and biochemistry, and inflammatory parameters.

Targeted metabolomic analyses will be conducted using ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) to identify and quantify polyphenol-derived metabolites, gut microbial metabolites, and other biochemical markers relevant to obesity and cardiometabolic risk.

Participants in the PBD group will receive a structured dietary plan featuring regional and national polyphenol-rich foods such as blueberries, hazelnuts, olive oil, apples, and coffee. The polyphenol supplementation group will receive a standardized supplement taken twice daily. The control group will receive placebo and standard nutritional counseling.

The study will adhere to ethical guidelines, including the Declaration of Helsinki and Good Clinical Practice principles. Ethical approval will be obtained, and all participants will provide informed consent. Data privacy will be ensured in compliance with applicable regulations.

Statistical analyses will be conducted on an intention-to-treat and per-protocol basis. Primary outcomes include changes in body weight and body composition, while secondary outcomes focus on cardiometabolic risk factors, dietary adherence, and metabolic signatures identified through metabolomics. Differences between pre- and post-intervention data, as well as between groups, will be analyzed using multivariate models, machine-learning algorithms, and generalized linear mixed models (GLMMs).

Inclusion criteria

• Age between 25 and 45 years (adult)
• Sexes: All
• Overweight or obesity class I (BMI 25-35 kg/m²)
• At least three of the following cardiometabolic risk factors:

Current smoker (more than 1 cigarette per day during the last month) Sitting time greater than 7 hours per day or classified as inactive according to the IPAQ Fasting glucose ≥100 mg/dL (5.6 mmol/L)

High blood pressure:

Systolic blood pressure >130 mmHg Diastolic blood pressure >85 mmHg High serum triglycerides (≥150 mg/dL) HbA1c ≥38 mmol/mol (>5.7%)

Waist circumference defined by Peterman et al. for the Chilean population:

92.3 cm for men 87.6 cm for women High high-sensitivity C-reactive protein (hs-CRP) (≥2.0 mg/L)

Family history of premature coronary heart disease:

Definite myocardial infarction or sudden death before age 55 in father or male first-degree relative Before age 65 in mother or female first-degree relative

Exclusion criteria

• Presence of cardiovascular disease, diabetes, or hypertension
• Severe medical conditions that may impair participation in a nutrition intervention study (e.g., digestive disease, advanced malignancy, major neurological or psychiatric disease)
• Any medical condition limiting survival to less than one year
• Illegal drug use, chronic alcoholism, or problematic alcohol use
• BMI ≥35 kg/m²
• Antibiotic use within the last month
• Low predicted likelihood of changing dietary habits according to the Prochaska and DiClemente stages of change model