The role of diet and dietary patterns in the composition of gut microbiota (GUTDIET-PT): A multicentre cross-sectional observational study

The role of diet and dietary patterns in the composition of gut microbiota (GUTDIET-PT): A multicentre cross-sectional observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol The role of diet and dietary patterns in the composition of gut microbiota (GUTDIET-PT): A multicentre cross-sectional observational study Mariana Moreira, Joana Serpa, André Salgado, Catarina Sousa Guerreiro, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169337/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The gut microbiome plays a fundamental role in human health, influencing immune, metabolic, and nutritional functions. Diet is a key factor in shaping microbiota composition, with dietary patterns like the Mediterranean Diet (MD) and Plant-Based Diets (PBD) associated with greater microbial diversity and beneficial bacteria. In contrast, the Western Diet (WD) has been associated with reduced diversity and increased risk of disease. Methods: The GUTDIET-PT study is a multicentre, cross-sectional observational study aiming to recruit 2,500 healthyparticipants aged 40 to 74. It is part of the GUTBIOME-PT (NCT06741293), a larger project designed to improve colorectal cancer (CRC) screening in Portugal. Socio-demographic and anthropometric data (weight and height) will be collected via self-administered questionnaires. Dietary intake will be assessed using two non-consecutive 24-hour dietary recalls, and principal component analysis (PCA) will be used to identify and characterise dietary patterns. Additionally, adherence to the MD and PBD will be evaluated using validated tools such as the Mediterranean Diet Adherence Score and the Plant-Based Dietary Index, while the inflammatory potential of the participants’ diet will be assessed through the Dietary Inflammatory Index. Gut microbial composition and functional capacity will be determined by shotgun metagenomic sequencing of faecal samples. Discussion: By combining advanced microbiota analysis with detailed dietary assessments through structured nutritional interviews, we aim to provide novel insights into the relationship between dietary patterns and gut microbiota composition and function. Additionally, focusing the study on healthy individuals will allow us to characterise the microbiota in the absence of disease, offering a clearer understanding of the features of a “healthy” microbiome. This study will establish a foundational framework for identifying dietary patterns that promote microbial diversity and functionality, contributing to tailored nutritional recommendations and public health strategies aimed at improving gut health. Trial Registration: NCT06741293 Gut Microbiota Dietary Patterns Mediterranean Diet Western Diet Plant-based Diet Dietary Inflammatory Index Figures Figure 1 Figure 2 Background Gut microbiota and its role in health and disease The gastrointestinal microbiota is a complex ecosystem composed of trillions of microorganisms that play a fundamental role in maintaining health and preventing disease. This community is largely dominated by two major bacterial phyla: Bacteroidota (Bacteroidetes), including genera such as Bacteroides and Prevotella , and Bacillota (Firmicutes), comprising genera like Clostridium, Enterococcus, Lactobacillus , and Ruminococcus . Together, these phyla account for approximately 90% of the gut microbial community. Other phyla include Pseudomonadota (Proteobacteria), Actinomycetota (Actinobacteria), with genera like Bifidobacterium , and Verrucomicrobiota (Verrucomicrobia) [1,2]. Gut microorganisms synthesise, transform, and metabolise essential nutrients and bioactive molecules, including lipids, bile acids, short-chain fatty acids (SCFAs), amino acids, and vitamins. Emerging evidence suggests that microbiota composition and metabolic activity exert substantial influence over immune regulation, nutritional homeostasis, and metabolic health [3]. Numerous diseases have been associated with gut microbiota imbalances, including gastrointestinal disorders like inflammatory bowel disease and irritable bowel syndrome [4,5], metabolic conditions such as type 2 diabetes Mellitus [6], various types of cancer [7,8], and neurological disorders like Parkinson's and Alzheimer’s disease [9]. Moreover, the gut microbiota may serve not only as a marker of current health status but also as a predictor of long-term health outcomes and longevity [10]. Although disease-related microbial signatures differ from those found in healthy individuals, there is still uncertainty when defining a ‘healthy’ microbiome [11]. Certain bacterial species may be associated with either health or disease, and different strains within the same species can exhibit distinct functionalities, which poses a challenge when it comes to associating species with health outcomes [12]. Nevertheless, the recently published consensus from the International Scientific Association for Probiotics and Prebiotics emphasises that gut health cannot be defined solely by microbiome composition, but rather reflects a broader state of normal gastrointestinal function, integrating microbial, host, and clinical factors. [13]. Interindividual variation in microbiota composition is shaped by genetics, age, diet, lifestyle, and environmental factors such as physical activity, stress, and medication use [14]. Among these, diet stands out as a modifiable factor with studies showing its major impact in defining the structure and function of the gut microbiota [15]. Understanding the mechanisms through which diet and dietary patterns influence microbial composition and functionality is therefore essential for developing targeted health-promoting strategies. Diet and the gut microbiota Diet exerts a major influence on gut microbiota composition, and the microbiota, in turn, modulates nutrient metabolism and bioavailability, forming a bidirectional relationship [3]. Dietary patterns - rather than isolated nutrients - are key determinants of microbial community structure, reflecting the complex combinations in which foods are habitually consumed. Both short-term dietary changes and long-term dietary patterns can substantially alter microbiota composition, although responses are highly individualised and may not lead to uniform community convergence across individuals [16–18]. A plant-based diet (PBD), that encompasses vegetarian, vegan, pescatarian, and flexitarian patterns [19], has been associated with lower risks of cardiovascular disease [20], metabolic syndrome [21], type 2 diabetes Mellitus [22], and CRC [23], as well as improved weight management [24]. A recent large-scale, multi-cohort metagenomic study reported that vegan dietary patterns are associated with the enrichment of several species-level genome bins, including known butyrate producers such as Butyricicoccus spp., Roseburia hominis and members of the Lachnospiraceae family that are highly specialised in fibre degradation [25]. The Mediterranean Diet (MD), often considered a PBD, is widely recognised as a leading dietary model for health promotion and disease prevention [12]. Originating from Mediterranean coastal countries, it emphasises nutrient-rich foods such as fruits, vegetables, whole grains and healthy fats (particularly olive oil), moderate consumption of poultry, fish and dairy products, and low intake of red meat. Its high intake of unprocessed foods contributes to its anti-inflammatory and antioxidant properties, highlighting its role in promoting overall health [26]. Adherence to the MD has been associated with greater microbial diversity and enrichment of beneficial genera, such as Bacteroides, Butyricoccus, Lachnoclostridium, Parasutterella, and Lachnospira [27,28] . Key components of PBD and MD, particularly dietary fibre, promote the growth of fibre-degrading bacteria that produce SCFAs such as propionate and butyrate, which support gut barrier integrity and intestinal homeostasis, and serve as energy sources for colonocytes and peripheral organs [29,30]. A randomised intervention study showed that adherence to the MD was associated with improved intestinal barrier integrity, reflected by reduced levels of lipopolysaccharide-binding protein and zonulin, alongside increased levels of propionate and butyrate, with these being identified as key mediators of this effect [31]. Polyphenols, abundant in olive oil, wine, fruits, nuts and vegetables, have antioxidant, anti-inflammatory and antibacterial properties and modulate microbiota composition [32]. While polyphenols themselves are poorly absorbed by the human body, they are metabolised by the gut microbiota, providing substrate for bacterial families such as Bifidobacteriaceae and Lactobacillaceae while reducing pathogenic bacteria like Escherichia coli and Clostridium perfringens [12,33]. Polyphenols also regulate microbial metabolites, including SCFAs, trimethylamine N-oxide (TMAO), and secondary bile acids, by modulating microbial enzyme activity [33]. Polyunsaturated fatty acids, including omega-3, also prominent in the MD, enhance gut epithelial barrier function, reduce inflammation, increase Bacteroidota, and decrease Bacillota-to-Bacteroidota ratio [34,35]. Intervention studies have shown MD-associated increases in Faecalibacterium prausnitzii and Roseburia faecis and reduced levels of Ruminococcus gnavus, Collinsella aerofaciens and Ruminococcus torques [36,37]. The Western Diet (WD), characterised by high consumption of refined sugars, animal fats, processed meats (especially red meat), refined grains, high-fat dairy products, and fried or pre-packaged foods and low intake of fruits, vegetables, whole grains, meat, fish, nuts, and seeds [38]. This dietary pattern is linked to a higher prevalence of non-communicable diseases, with systematic reviews and meta-analyses showing associations between the WD and increased risk of breast cancer [39], metabolic syndrome [40], gestational diabetes [41], CRC [23], and overall mortality among cancer survivors [42]. Red meat-derived compounds, such as choline and carnitine, can be transformed into trimethylamine and subsequently into TMAO, an organic compound associated with chronic diseases and cardiovascular risk [43]. Furthermore, nitrates and nitrites used in processed meats can be transformed by bacteria into carcinogenic N-nitroso compounds that are associated with a higher risk of GI cancers [44]. Beyond assessing adherence to specific dietary patterns, it is also important to evaluate the overall inflammatory potential of the diet [45]. Diets rich in fibre, polyphenols, and unsaturated fatty acids generally exert anti-inflammatory effects and are linked to a more favourable gut microbiome profile. In contrast, dietary patterns high in saturated fats, refined carbohydrates, and processed meats are associated with increased systemic inflammation [46]. Given the bidirectional relationship between inflammation and gut microbiota composition [47], assessing the inflammatory potential of the overall diet may provide additional insight into how dietary patterns influence microbial diversity and functional capacity. In this study, we will investigate how dietary patterns and their inflammatory potential interplay with the gut microbiota. Understanding these relationships is a crucial step as it helps to shape hypotheses and refine methodologies for future prospective research and can guide the development of public health initiatives, preventive strategies, dietary recommendations, and targeted interventions. Study Aims The primary aim of this study is to investigate how different dietary patterns associate with the composition and functionality of the gut microbiota. Specifically, we will (1) characterise the gut microbiota of the study population, (2) identify prevalent dietary patterns within the study sample, and (3) analyse associations between these dietary patterns, specific dietary constituents, and faecal microbiome profiles. Methods Study Design This cross-sectional observational study, conducted at the Gulbenkian Institute for Molecular Medicine, is part of the “Improving Colorectal Cancer Early Screening in Portugal: Identification and Validation of Gut Microbiome Biomarkers (GUTBIOME-PT)” project (NCT06741293). This project aims to recruit 30,000 participants over a period of 6 years. Recruitment started in November 2023 and is planned to be completed by 2029. From the 30,000 participants recruited for GUTBIOME-PT, we aim to recruit 2500 individuals with a negative colonoscopy result for this study. The project is approved by the Ethics Committee of the Academic Medicine Center of Lisbon (Ref 111/23). All procedures comply with the principles of the Helsinki Declaration, ensuring informed consent, respect, integrity, privacy and confidentiality for all participants. Participants retain the right to withdraw from the study at any time without penalty. Recruitment and eligibility criteria Participants are recruited at partner hospitals, among patients who are referred for a screening colonoscopy and do not have a clinical diagnosis of CRC or a first-degree family history of CRC. Eligible participants are selected from the control group of the GUTBIOME-PT study, consisting of individuals with negative colonoscopy results. Participants who meet all eligibility criteria (Table 1) receive detailed information about the study and are invited to sign the informed consent form. Table 1: Inclusion and exclusion criteria for eligibility Inclusion criteria 1. To sign the informed consent 2. Resident in the metropolitan area of Lisbon, Portugal 3. Participants 40 to 74 years 4. Be referred for a screening colonoscopy Exclusion criteria 1. < 40 or ≥ 75 years old 2. Active oncological disease 3.Personal or first-degree family history of CRC 4. Intestinal adenomas removed in the last 24 months 5. Diagnosis of inflammatory bowel disease, irritable bowel syndrome or recurrent infection by Clostridioides difficile 6. Severe cardiovascular or heart diseases 7. Severe renal failure requiring hemodialysis 8. Severe lung disease 9. Pregnancy The overall diagram flow of the study is shown in Fig. 1. and in the SPIRIT figure illustrated in Fig. 2. The SPIRIT checklist is provided as supplementary material. Data collection Sociodemographic, lifestyle, and health-related characteristics: data is collected using self-administered questionnaires completed by participants through a GDPR-compliant online platform developed specifically for the GUTBIOME-PT study. The questionnaires are structured into three main sections: sociodemographic characteristics such as education level and economic status, clinical information including any previous or current medical condition, and lifestyle factors such as smoking status, sleep, stress, and physical activity. Body mass index Each participant self-reports their weight and height in a health questionnaire following stool sample collection. Body Mass Index (BMI) is calculated using the formula (BMI = weight (kg) / height² (m)) and is categorised based on the World Health Organisation's cutoffs [48]. Dietary intake Dietary intake data is collected through telephone interviews conducted by a qualified clinical nutritionist, following validated protocols by the European Food Safety Authority [49] and the Portuguese National Food and Physical Activity Survey [50]. Two non-consecutive 24-hour dietary recalls are performed, recording all foods, beverages, and dietary supplements consumed during the day - from 00h00 to 23h59 - preceding each interview. Each food item is documented with details on the context of consumption (location and time), quantity consumed, and preparation and cooking methods. Common volume measures (e.g., cups, tablespoons, teaspoons, palm) are used to estimate portion sizes. If possible, the brand of the product eaten is recorded. The conversion of food into nutrients is made with nutritional information from the Portuguese Food Composition Table (FCT), available on the PortFIR website [51], version 7.0. For foods not included in the Portuguese FCT, the French [52], and British [53] food composition tables are used. Adherence to the MD MD adherence is assessed using the 14-Item Mediterranean Diet Adherence Score developed by the PREvención con DIeta MEDiterránea (PREDIMED) study authors [54], which includes 12 questions on food consumption and frequency, and two questions on food intake habits that are key principles of the MD. Responses are scored 0 (condition not met) or 1 (condition met), resulting in a score ranging from 0 to 14. Based on this final score, adherence to the MD is categorised as low (score 10). This questionnaire is applied to participants during the first 24-hour dietary recall. Adherence to a PBD Adherence to a PBD is assessed according to the method published by Satija et al . using a plant-based dietary index (PDI), a healthful PDI (hPDI), and an unhealthful PDI (uPDI) [55]. The individual foods collected through the 24-hour dietary recalls are combined into groups based on animal foods, healthy plant foods, and unhealthy plant foods. Based on the intake levels of these food groups, all food groups are divided into quintiles and assigned separate positive scores (i.e., higher intake receives higher scores, with a score range of 1–5) or reverse scores (i.e., higher intake receives lower scores, with a score range of 5–1). For the overall PDI, all plant-based foods are assigned positive scores, whereas animal-based foods are assigned negative scores, and the scores are then summed to obtain an overall PDI score. For the hPDI, positive scores are assigned to healthy plant foods, whereas negative scores are assigned to animal and unhealthy plant foods. For the uPDI, positive scores are assigned to unhealthy plant foods, whereas negative scores are assigned to animal and healthy plant foods. For all three PDIs, higher scores indicate greater adherence to dietary patterns [55,56]. Dietary inflammatory index Dietary inflammatory potential is assessed using the Dietary Inflammatory Index (DII), as developed by Shivappa et al . [57]. Dietary intake data derived from the 24-hour dietary recalls are used to calculate individual DII scores. Intakes of available dietary components are standardised to a global reference database by calculating z-scores based on global means and standard deviations. These z-scores are converted to centred percentiles to account for skewed intake distributions. Each centred percentile is then multiplied by a literature-derived inflammatory effect score specific to each dietary component, reflecting its pro- or anti-inflammatory association. To account for total energy intake, an energy-adjusted DII (E-DII) is also calculated by expressing dietary intakes per 1,000 kcal and applying energy-adjusted global reference values. Only dietary components available from the dietary assessment are included in the DII calculation, and missing components are not imputed. The resulting component-specific scores are summed to generate an overall DII and E-DII score, with higher positive values indicating a more pro-inflammatory diet [57]. Stool sample characterisation Participants self‑collect a stool sample using a provided kit (EasySampler® stool collection kit with DNA stabilisation buffer, Invitek), which preserves microbial DNA integrity at the moment of collection. Stool consistency is assessed using the Bristol Stool Scale (BSS) scores [58], which categorises stool into seven types as a proxy for intestinal transit rate [59]. Stool samples are also characterised by colour and recent gastrointestinal symptoms are annotated, such as bloating, cramps or abdominal pain, excessive intestinal gas, mucus in the stool, abnormal stool colour, and presence of blood in the stool. Frequency of bowel movements over the past 5 days is also considered. Faecal DNA extraction and sequencing Once collected, the stool samples are transported to the laboratory, where they are subdivided into aliquots and stored at -80°C until further analysis. Samples are processed for DNA extraction within a maximum of 6 months. For faecal microbiome characterisation, samples undergo genomic DNA extraction using the ZymoBIOMICS TM 96 MagBead DNA Kit (Zymo Research), compatible with the KingFisher™ Flex automatic extractor (Thermo Fisher Scientific). DNA concentrations are measured using a Qubit TM 1X dsDNA HS assay kit (Invitrogen TM ), on a Qubit TM Flex Fluorometer (Life Technologies). The composition and genetic content of the faecal microbiota are analysed through shotgun metagenomic sequencing, performed on an Illumina platform (NovaSeq X Plus), with a minimum of 3 million reads per sample. Data management Participant withdrawal In line with the Declaration of Helsinki, participants are informed that they may withdraw from the study at any time. Reasons for withdrawal are reported in the source documents and on the case report form. Samples from participants who withdraw are preserved and may still be analysed, as long as the participant does not request the destruction of their data. Missing Data If a sample is missing, associated data (e.g., diet-related questionnaires) is excluded from the analysis, and the participant is considered withdrawn to maintain data integrity, as biological samples are essential for the study. Additionally, participants who do not complete mandatory questionnaires are considered excluded from the study, and their sample is not considered for analysis. If the participant completes only one 24-hour dietary recall, this data is included in the analysis. Although multiple interviews are preferred for accuracy, a single interview can still provide relevant insights into dietary habits [49]. Data codification Participant data is confidential and identified only by a unique participant code ("Participant ID") on both faecal samples, questionnaires, and the electronic database. Data from questionnaires is recorded on a secure online platform, with sample results stored in a separate database linked through the participant's code to ensure participant anonymity. Personal data is processed based on the participant’s explicit consent, strictly for scientific research, study-related communication, and compliance with legal requirements and regulatory obligations. Data Storage Personal data is stored with restricted access in compliance with the General Data Protection Regulation and relevant legislation. All data remains within the originating institution in compliance with the data management plan, which outlines protocols for data handling, storage, and sharing. To increase accessibility, pseudonymisation is employed to make the data more widely available. Due to intellectual property constraints, data may not be fully open access. Data and document ownership are clearly assigned, and a designated individual will manage data transfer at project completion. Data will be archived in cold storage for long-term preservation. This approach will help ensure the data remains comprehensible and reusable over time. Personal information is processed in a pseudonymised format, with access to decryption keys restricted to authorised personnel. This project is being conducted in collaboration with multiple institutions under a formal collaboration agreement that outlines data access permissions, with ongoing updates as needed. Data analysis Sample size Sample size estimation was informed by power analyses using the Shannon Diversity Index as the primary outcome. Based on the effect size reported by Malinowska et al . [60] (mean difference ≈ 0.08 units; Cohen’s d ≈ 0.17), balanced two‑group comparisons achieved ≥90% power with 1500-2000 participants. However, we acknowledge that power will be reduced under unbalanced group allocations. To ensure adequate statistical power while accounting for attrition, we applied a 20% anticipated drop-out rate. Therefore, to retain an effective sample size of 2000 participants, the study will aim to recruit approximately: 2000 ÷ 0.80 = 2,500 participants Thus, the target recruitment number is 2,500 participants, allowing sufficient power for primary comparisons even with expected loss to follow-up or incomplete data. Software Statistical analysis is performed using RStudio (R Foundation for Statistical Computing) and Python, with p < 0.05 considered statistically significant. R is used for statistical testing, data analysis, and visualisation, while Python supports advanced data analysis and statistical modelling to enhance the robustness of the findings and enable in-depth biological interpretation. Microbiome Analysis The faecal DNA obtained from participants undergoes shotgun metagenomic sequencing [61], generating approximately 3 to 4 gigabytes of data per sample. Paired-end sequencing is performed, and the resulting raw sequences are processed to ensure high-quality data. Fastp is employed to trim and remove low-quality reads [62], and bowtie2 is used to eliminate host contamination by aligning the sample reads to the human genome reference (GRCh38) [63]. To quantify the abundance of microbial taxa, a hybrid approach combining k-mer analysis and mapping techniques is applied to the preprocessed sequences, utilising publicly available genome databases such as GTDB [64] and UHGG [65] for reference support. The functional potential of the microbiomes is inferred by directly mapping the metagenomic data to functional databases such as Uniprot [66], KEGG [67], or CAZy [68] and performing gene annotation and characterisation of the assembled metagenomes. Statistics The statistical analysis focuses on evaluating the associations between dietary patterns and gut microbiota composition and function. Major dietary patterns present in the study sample will be identified and characterised using PCA with varimax rotation, a posteriori approach (data-driven) that is a variable reduction procedure based on correlation or covariance matrices of the original variables, creating linear combinations such as patterns [69]. The individual foods collected through the 24-hour dietary recalls will be combined into groups based on their nutritional components- similarity and culinary uses, and these groups will serve as variables for the PCA, as previously described [70]. Alternatively, clustering techniques (e.g., k-means) may be employed to classify participants into distinct dietary groups. Microbial alpha diversity (within-sample diversity) will be assessed using Shannon, Simpson, and observed richness indices, while beta diversity (between-sample differences) will be calculated using Bray-Curtis dissimilarity and weighted/unweighted UniFrac metrics. Differences in alpha diversity between dietary groups will be evaluated using the Kruskal-Wallis test, and beta diversity will be compared using PERMANOVA (Permutational Multivariate Analysis of Variance), with visualisation through Principal Coordinate Analysis (PCoA) plots. Differential abundance analysis will be conducted to identify taxa and functional pathways associated with specific dietary patterns while controlling for multiple comparisons with the Benjamini-Hochberg correction. Correlation analyses (Spearman or Pearson) will be performed to explore the relationships between dietary intake (e.g., macronutrient composition) and the relative abundance of microbial taxa and functional features. Multivariable models, including linear or logistic regression, will adjust for potential confounders such as age, sex, BMI, and physical activity to ensure robust associations. Additionally, machine learning approaches, such as Random Forests, will be applied to identify key microbial taxa or functions predictive of dietary patterns. Statistical significance will be set at p < 0.05 unless otherwise stated. Potential outcomes and clinical impact To our knowledge, this is the first large-scale observational study to examine how distinct dietary patterns relate to gut microbiota composition and function in the Portuguese population using shotgun metagenomic sequencing. The sample size and methodological rigour provide a robust foundation for characterising diet–microbiota interactions and generating evidence that may inform future research, nutritional guidance, and public health strategies. Shotgun metagenomic sequencing offers higher taxonomic and functional resolution than 16S rRNA amplicon sequencing, allowing the detection of low-abundance taxa and enabling detailed functional profiling [71,72]. This methodology enhances the capacity to identify structural variation and metabolic pathways within the microbiome [61]. Combined with detailed dietary information obtained through structured nutritional interviews, this approach enables a comprehensive assessment of the relationship between dietary patterns and microbiota composition and function. Previous studies have demonstrated that specific nutrients, such as fibre or saturated fat, and foods, such as red meat, are associated with distinct microbiota compositions [43,73]. However, focusing only on individual nutrients or foods may fail to explain their overall effect on health, since it does not take into account the interaction between nutrients and foods. Studying dietary patterns provides a more holistic and comprehensive approach. Research has analysed the associations between dietary patterns - such as MD, PBD and WD - and microbiota composition, but these studies often focus on specific diseases, such as cancer or metabolic disorders [3,74]. By contrast, the present study focuses on healthy adults, allowing us to characterise microbiota composition in the absence of disease and to identify dietary behaviours that support microbial diversity and functional capacity. This approach contributes to a clearer understanding of microbiome characteristics associated with health and provides a foundational framework for identifying dietary patterns that promote microbial diversity and functional capacity. Furthermore, large population-based metagenomic studies remain limited in Mediterranean countries. By examining the Portuguese population within its specific cultural and dietary context, this study helps address a national knowledge gap while also expanding the evidence base for microbiome research. This contributes to a broader and more nuanced understanding of microbiota variability across Mediterranean populations. Dietary intake will be assessed using two non‑consecutive 24‑hour dietary recalls, a validated method that captures detailed information on consumed foods and beverages [49]. Compared with food frequency questionnaires, which may lack precision and restrict responses to predefined items, 24‑hour recalls allow for more accurate, flexible assessment of dietary behaviours, including the detection of non‑listed foods [75,76]. This short-term resolution is particularly relevant given the rapid responsiveness of the gut microbiota to recent dietary intake, which long-term dietary tools may not accurately capture [77]. Although recall-based methods are subject to reporting bias [75,78], this limitation will be mitigated through validated protocols [49], structured interviews conducted by trained nutritionists, and statistical adjustments for potential confounders. While some participants may provide only a single recall, the large sample size of the study is expected to reduce variability and support reliable dietary characterisation. Altogether, this study is expected to generate valuable insights into the complex interactions between diet and gut microbiota. By clarifying how dietary patterns relate to microbial composition and function, the findings may inform evidence‑based public health and nutritional strategies aimed at promoting gut health. In the longer term, this work has the potential to support precision nutrition approaches tailored to individual dietary habits and microbiota profiles, ultimately enhancing the effectiveness of dietary interventions designed to improve health and well‑being. Abbreviations BMI - Body Mass Index BSS - Bristol Stool Scale CRC - Colorectal Cancer DII - Dietary Inflammatory Index hPDI - Healthful Plant-Based Dietary Index MD - Mediterranean Diet PBD - Plant-Based Diet PDI – Plant-Based Dietary Index PREDIMED - PREvención con DIeta MEDiterránea SCFA - Short-Chain Fatty Acids TMAO - Trimethylamine N-oxide uPDI - Unhealthful Plant-Based Dietary Index WD - Western Diet Declarations Ethics approval and consent to participate The project was approved by the Ethics Committee of the Academic Medicine Center of Lisbon (Ref 111/23) in November 2023. All procedures comply with the principles of the Helsinki Declaration, ensuring informed consent, respect, integrity, privacy and confidentiality for all participants. Consent for publication Unless specified by local laws or regulations, the Sponsor will be responsible for ownership of the data, results, reports, conclusions, or findings related to this study. Consequently, the Sponsor reserves the right to ownership and use of the data from this study for the purpose described in the project. Access to the data obtained may be granted to other qualified researchers proposing valid scientific analyses, always respecting the confidentiality of the data and the anonymity of the participants. Information regarding the identification of participants will never be shared, respecting the data confidentiality mentioned above. Availability of data and materials Not applicable Competing interests The authors declare that they have no competing interests. Funding This work is funded by GIMM-CARE. GIMM-CARE is funded by the European Union under grant agreement No. 101060102. GIMM-CARE is co-funded by the Portuguese Government, the National Foundation for Science and Technology (FCT), ARICA – Investimentos, Participações e Gestão, Jerónimo Martins, the Gulbenkian Institute for Molecular Medicine and CAML - Lisbon Academic Medical Centre. ASA is supported by a grant from Fundação para a Ciência e Tecnologia (2021.02791.CEECIND). AS is supported by a grant from Fundação para a Ciência e Tecnologia (2025.02293.BD). Authors’ contributions CSG, IS and ASA contributed to the conception and design of the study. MMM and JS drafted the manuscript, and all authors critically read and revised it. All authors approved the final version of the manuscript. Acknowledgements We would like to sincerely thank the Mission Lab Team for handling and processing the faecal samples, and the Biobank at GIMM-Care, where all biological samples will be stored. We also thank the Gastroenterology and General Medicine Teams at our partner hospitals (Hospital da Luz, CUF Tejo, and CUF Descobertas) for recruiting study participants, as well as all study coordinators for participant registration. We are grateful to the Advanced Data Analysis team from the The Digital Technologies Unit at GIMM for their support with the sample size calculation, and to the members of the Microbiome in Health and Disease Translational Laboratory for their critical input on the manuscript. Finally, we extend our sincere appreciation to all participants of the GUTBIOME-PT study who kindly agreed to take part in this research. References Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. https://doi.org/10.1038/nature08821 Tap J, Mondot S, Levenez F, Pelletier E, Caron C, Furet J, et al. Towards the human intestinal microbiota phylogenetic core. Environ Microbiol. 2009;11:2574–84. https://doi.org/10.1111/j.1462-2920.2009.01982.x Breuninger TA, Wawro N, Breuninger J, Reitmeier S, Clavel T, Six-Merker J, et al. Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation. Microbiome. 2021;9:61. https://doi.org/10.1186/s40168-020-00969-9 Iliev ID, Ananthakrishnan AN, Guo C-J. Microbiota in inflammatory bowel disease: mechanisms of disease and therapeutic opportunities. Nat Rev Microbiol. 2025;23:509–24. https://doi.org/10.1038/s41579-025-01163-0 Duan R, Zhu S, Wang B, Duan L. Alterations of Gut Microbiota in Patients With Irritable Bowel Syndrome Based on 16S rRNA-Targeted Sequencing: A Systematic Review. Clin Transl Gastroenterol. 2019;10:e00012. https://doi.org/10.14309/ctg.0000000000000012 Mei Z, Wang F, Bhosle A, Dong D, Mehta R, Ghazi A, et al. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes. Nat Med. 2024;30:2265–76. https://doi.org/10.1038/s41591-024-03067-7 Fernandez E, Wargo JA, Helmink BA. The Microbiome and Cancer. JAMA. 2025;333:2188–96. https://doi.org/10.1001/jama.2025.2191 Nobels A, Marcke C van, Jordan BF, Hul MV, Cani PD. The gut microbiome and cancer: from tumorigenesis to therapy. Nat Metab. 2025;7:895–917. https://doi.org/10.1038/s42255-025-01287-w Cryan JF, O’Riordan KJ, Sandhu K, Peterson V, Dinan TG. The gut microbiome in neurological disorders. Lancet Neurol. 2020;19:179–94. https://doi.org/10.1016/s1474-4422(19)30356-4 Joos R, Boucher K, Lavelle A, Arumugam M, Blaser MJ, Claesson MJ, et al. Examining the healthy human microbiome concept. Nat Rev Microbiol. 2024;1–14. https://doi.org/10.1038/s41579-024-01107-0 Gacesa R, Kurilshikov A, Vila AV, Sinha T, Klaassen MAY, Bolte LA, et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature. 2022;604:732–9. https://doi.org/10.1038/s41586-022-04567-7 Ross FC, Patangia D, Grimaud G, Lavelle A, Dempsey EM, Ross RP, et al. The interplay between diet and the gut microbiome: implications for health and disease. Nat Rev Microbiol. 2024;1–16. https://doi.org/10.1038/s41579-024-01068-4 Marco ML, Cunningham M, Bischoff SC, Clarke G, Delzenne N, Lewis JD, et al. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of gut health. Nat Rev Gastroenterol Hepatol. 2026;1–17. https://doi.org/10.1038/s41575-026-01176-x Mousa WK, Chehadeh F, Husband S. Recent Advances in Understanding the Structure and Function of the Human Microbiome. Front Microbiol. 2022;13:825338. https://doi.org/10.3389/fmicb.2022.825338 Zmora N, Suez J, Elinav E. You are what you eat: diet, health and the gut microbiota. Nat Rev Gastroenterol Hepatol. 2019;16:35–56. https://doi.org/10.1038/s41575-018-0061-2 Rinninella E, Tohumcu E, Raoul P, Fiorani M, Cintoni M, Mele MC, et al. The role of diet in shaping human gut microbiota. Best Pr Res Clin Gastroenterol. 2023;62:101828. https://doi.org/10.1016/j.bpg.2023.101828 Senghor B, Sokhna C, Ruimy R, Lagier J-C. Gut microbiota diversity according to dietary habits and geographical provenance. Hum Microbiome J. 2018;7:1–9. https://doi.org/10.1016/j.humic.2018.01.001 Vermeulen A, Bootsma E, Proost S, Vieira-Silva S, Kathagen G, Vázquez-Castellanos JF, et al. Dietary convergence induces individual responses in faecal microbiome composition. eGastroenterology. 2025;3:e100161. https://doi.org/10.1136/egastro-2024-100161 Zhao Y, Zhan J, Wang Y, Wang D. The Relationship Between Plant-Based Diet and Risk of Digestive System Cancers: A Meta-Analysis Based on 3,059,009 Subjects. Front Public Heal. 2022;10:892153. https://doi.org/10.3389/fpubh.2022.892153 Quek J, Lim G, Lim WH, Ng CH, So WZ, Toh J, et al. The Association of Plant-Based Diet With Cardiovascular Disease and Mortality: A Meta-Analysis and Systematic Review of Prospect Cohort Studies. Front Cardiovasc Med. 2021;8:756810. https://doi.org/10.3389/fcvm.2021.756810 Nikparast A, Etesami E, Rahmani J, Rafiei N, Ghanavati M. The association between plant-based diet indices and metabolic syndrome: a systematic review and dose–response meta-analysis. Front Nutr. 2024;10:1305755. https://doi.org/10.3389/fnut.2023.1305755 Qian F, Liu G, Hu FB, Bhupathiraju SN, Sun Q. Association Between Plant-Based Dietary Patterns and Risk of Type 2 Diabetes. JAMA Intern Med. 2019;179:1335–44. https://doi.org/10.1001/jamainternmed.2019.2195 Garcia-Larsen V, Morton V, Norat T, Moreira A, Potts JF, Reeves T, et al. Dietary patterns derived from principal component analysis (PCA) and risk of colorectal cancer: a systematic review and meta-analysis. Eur J Clin Nutr. 2019;73:366–86. https://doi.org/10.1038/s41430-018-0234-7 Tran E, Dale HF, Jensen C, Lied GA. Effects of Plant-Based Diets on Weight Status: A Systematic Review. Diabetes, Metab Syndr Obes: Targets Ther. 2020;13:3433–48. https://doi.org/10.2147/dmso.s272802 Fackelmann G, Manghi P, Carlino N, Heidrich V, Piccinno G, Ricci L, et al. Gut microbiome signatures of vegan, vegetarian and omnivore diets and associated health outcomes across 21,561 individuals. Nat Microbiol. 2025;10:41–52. https://doi.org/10.1038/s41564-024-01870-z Trichopoulou A, Martínez-González MA, Tong TY, Forouhi NG, Khandelwal S, Prabhakaran D, et al. Definitions and potential health benefits of the Mediterranean diet: views from experts around the world. BMC Med. 2014;12:112. https://doi.org/10.1186/1741-7015-12-112 Bourdeau-Julien I, Castonguay-Paradis S, Rochefort G, Perron J, Lamarche B, Flamand N, et al. The diet rapidly and differentially affects the gut microbiota and host lipid mediators in a healthy population. Microbiome. 2023;11:26. https://doi.org/10.1186/s40168-023-01469-2 Armet AM, Deehan EC, O’Sullivan AF, Mota JF, Field CJ, Prado CM, et al. Rethinking healthy eating in light of the gut microbiome. Cell Host Microbe. 2022;30:764–85. https://doi.org/10.1016/j.chom.2022.04.016 Wang DD, Nguyen LH, Li Y, Yan Y, Ma W, Rinott E, et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat Med. 2021;27:333–43. https://doi.org/10.1038/s41591-020-01223-3 Besten G den, Eunen K van, Groen AK, Venema K, Reijngoud D-J, Bakker BM. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res. 2013;54:2325–40. https://doi.org/10.1194/jlr.r036012 Seethaler B, Nguyen NK, Basrai M, Kiechle M, Walter J, Delzenne NM, et al. Short-chain fatty acids are key mediators of the favorable effects of the Mediterranean diet on intestinal barrier integrity: data from the randomized controlled LIBRE trial. Am J Clin Nutr. 2022;116:928–42. https://doi.org/10.1093/ajcn/nqac175 Catalkaya G, Venema K, Lucini L, Rocchetti G, Delmas D, Daglia M, et al. Interaction of dietary polyphenols and gut microbiota: Microbial metabolism of polyphenols, influence on the gut microbiota, and implications on host health. Food Front. 2020;1:109–33. https://doi.org/10.1002/fft2.25 Rudrapal M, Oliveira AM de, Singh RP. Dietary polyphenols maintain human health through modulation of gut microbiota. Front Pharmacol. 2025;16:1710088. https://doi.org/10.3389/fphar.2025.1710088 Liu H, Li X, Zhu Y, Huang Y, Zhang Q, Lin S, et al. Effect of Plant-Derived n-3 Polyunsaturated Fatty Acids on Blood Lipids and Gut Microbiota: A Double-Blind Randomized Controlled Trial. Front Nutr. 2022;9:830960. https://doi.org/10.3389/fnut.2022.830960 Telle-Hansen VH, Gaundal L, Bastani N, Rud I, Byfuglien MG, Gjøvaag T, et al. Replacing saturated fatty acids with polyunsaturated fatty acids increases the abundance of Lachnospiraceae and is associated with reduced total cholesterol levels—a randomized controlled trial in healthy individuals. Lipids Heal Dis. 2022;21:92. https://doi.org/10.1186/s12944-022-01702-1 Ghosh TS, Rampelli S, Jeffery IB, Santoro A, Neto M, Capri M, et al. Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries. Gut. 2020;69:1218–28. https://doi.org/10.1136/gutjnl-2019-319654 Meslier V, Laiola M, Roager HM, Filippis FD, Roume H, Quinquis B, et al. 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;69:1258–68. https://doi.org/10.1136/gutjnl-2019-320438 García-Montero C, Fraile-Martínez O, Gómez-Lahoz AM, Pekarek L, Castellanos AJ, Noguerales-Fraguas F, et al. Nutritional Components in Western Diet Versus Mediterranean Diet at the Gut Microbiota–Immune System Interplay. Implications for Health and Disease. Nutrients. 2021;13:699. https://doi.org/10.3390/nu13020699 Xiao Y, Xia J, Li L, Ke Y, Cheng J, Xie Y, et al. Associations between dietary patterns and the risk of breast cancer: a systematic review and meta-analysis of observational studies. Breast Cancer Res. 2019;21:16. https://doi.org/10.1186/s13058-019-1096-1 Ushula TW, Mamun A, Darssan D, Wang WYS, Williams GM, Whiting SJ, et al. Dietary patterns and the risks of metabolic syndrome and insulin resistance among young adults: Evidence from a longitudinal study. Clin Nutr. 2022;41:1523–31. https://doi.org/10.1016/j.clnu.2022.05.006 Quan W, Zeng M, Jiao Y, Li Y, Xue C, Liu G, et al. Western Dietary Patterns, Foods, and Risk of Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis of Prospective Cohort Studies. Adv Nutr. 2021;12:1353–64. https://doi.org/10.1093/advances/nmaa184 Schwedhelm C, Boeing H, Hoffmann G, Aleksandrova K, Schwingshackl L. Effect of diet on mortality and cancer recurrence among cancer survivors: a systematic review and meta-analysis of cohort studies. Nutr Rev. 2016;74:737–48. https://doi.org/10.1093/nutrit/nuw045 Severino A, Tohumcu E, Tamai L, Dargenio P, Porcari S, Rondinella D, et al. The microbiome-driven impact of western diet in the development of noncommunicable chronic disorders. Best Pr Res Clin Gastroenterol. 2024;72:101923. https://doi.org/10.1016/j.bpg.2024.101923 Zhang Y, Zhang Y, Jia J, Peng H, Qian Q, Pan Z, et al. Nitrite and nitrate in meat processing: Functions and alternatives. Curr Res Food Sci. 2023;6:100470. https://doi.org/10.1016/j.crfs.2023.100470 Vázquez-Lorente H, Hernández-Cacho A, García-Gavilán JF, Li J, Ruiz-Canela M, Belzer C, et al. Inflammatory dietary potential and gut microbiota in older adults with overweight or obesity and metabolic syndrome. Food Res Int. 2025;221:117263. https://doi.org/10.1016/j.foodres.2025.117263 Mirhosseini SM, Mahdavi A, Yarmohammadi H, Razavi A, Rezaei M, Soltanipur M, et al. What is the link between the dietary inflammatory index and the gut microbiome? A systematic review. Eur J Nutr. 2024;63:2407–19. https://doi.org/10.1007/s00394-024-03470-3 Hul MV, Cani PD, Petitfils C, Vos WMD, Tilg H, El-Omar EM. What defines a healthy gut microbiome? Gut. 2024;73:1893–908. https://doi.org/10.1136/gutjnl-2024-333378 CB W, A J. BMI Classification Percentile and Cut Off Points [Internet]. Bethesda (MD): National Center for Biotechnology Information; 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK541070/. Accessed November 07, 2024. Authority EFS. General principles for the collection of national food consumption data in the view of a pan‐European dietary survey. EFSA J. 2009;7:1435. https://doi.org/10.2903/j.efsa.2009.1435 Lopes C, Torres D, Oliveira A, Severo M, Alarcão V, Guiomar S, et al. Inquérito Alimentar Nacional e de Atividade Física, IAN-AF 2015- 2016: Relatório metodológico. Universidade do Porto, 2017. ISBN: 978-989-746-180-4. Disponível em: www.ian-af.up.pt. Acessed November 7,2024. 2017; Instituto Nacional de Saúde Doutor Ricardo Jorge (INSA). Disponível nova versão da Tabela de Composição de Alimentos [Internet]. Lisboa: INSA; 2023. Available from: https://www.insa.min-saude.pt/disponivel-nova-versao-da-tabela-de-composicao-de-alimentos/. Accessed October 15, 2024. ANSES-CIQUAL. CIQUAL Food Composition Table [Internet]. Maisons-Alfort: French Agency for Food, Environmental and Occupational Health & Safety; 2020. Available from: https://ciqual.anses.fr/. Accessed October 15, 2024. Public Health England (PHE). McCance and Widdowson’s The Composition of Foods Integrated Dataset (CoFID) [Internet]. London: PHE; 2021. Available from: https://www.gov.uk/government/publications/composition-of-foods-integrated-dataset-cofid. Accessed October 15, 2024. Martínez-González MA, García-Arellano A, Toledo E, Salas-Salvadó J, Buil-Cosiales P, Corella D, et al. A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial. PLoS ONE. 2012;7:e43134. https://doi.org/10.1371/journal.pone.0043134 Satija A, Bhupathiraju SN, Rimm EB, Spiegelman D, Chiuve SE, Borgi L, et al. Plant-Based Dietary Patterns and Incidence of Type 2 Diabetes in US Men and Women: Results from Three Prospective Cohort Studies. PLoS Med. 2016;13:e1002039. https://doi.org/10.1371/journal.pmed.1002039 Li X, Li M, Cheng J, Guan S, Hou L, Zu S, et al. Association of healthy and unhealthy plant-based diets with telomere length. Clin Nutr. 2024;43:1694–701. https://doi.org/10.1016/j.clnu.2024.06.004 Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Heal Nutr. 2014;17:1689–96. https://doi.org/10.1017/s1368980013002115 Tigchelaar EF, Bonder MJ, Jankipersadsing SA, Fu J, Wijmenga C, Zhernakova A. Gut microbiota composition associated with stool consistency. Gut. 2016;65:540. https://doi.org/10.1136/gutjnl-2015-310328 Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016;65:57. https://doi.org/10.1136/gutjnl-2015-309618 Malinowska AM, Kok DE, Steegenga WT, Hooiveld GJEJ, Chmurzynska A. Human gut microbiota composition and its predicted functional properties in people with western and healthy dietary patterns. Eur J Nutr. 2022;61:3887–903. https://doi.org/10.1007/s00394-022-02928-6 Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833–44. https://doi.org/10.1038/nbt.3935 Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90. https://doi.org/10.1093/bioinformatics/bty560 Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9. https://doi.org/10.1038/nmeth.1923 Parks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil P-A, Hugenholtz P. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 2021;50:D785–94. https://doi.org/10.1093/nar/gkab776 Almeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, Shi ZJ, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol. 2021;39:105–14. https://doi.org/10.1038/s41587-020-0603-3 Consortium TU. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47:D506–15. https://doi.org/10.1093/nar/gky1049 Kanehisa M. ‘In Silico’ Simulation of Biological Processes. Novartis Found Symp. 2016;91–103. https://doi.org/10.1002/0470857897.ch8 Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233–8. https://doi.org/10.1093/nar/gkn663 Velho S, Moço S, Ferreira A, Cruz R, Agostinho L, Cabral MS, et al. Dietary patterns and their relationships to sarcopenia in Portuguese patients with gastrointestinal cancer: An exploratory study. Nutrition. 2019;63:193–9. https://doi.org/10.1016/j.nut.2019.01.014 Santos R de O, Gorgulho BM, Castro MA de, Fisberg RM, Marchioni DM, Baltar VT. Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application. Rev Bras Epidemiologia. 2019;22:e190041. https://doi.org/10.1590/1980-549720190041 Bars-Cortina D, Ramon E, Rius-Sansalvador B, Guinó E, Garcia-Serrano A, Mach N, et al. Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota. BMC Genom. 2024;25:730. https://doi.org/10.1186/s12864-024-10621-7 Durazzi F, Sala C, Castellani G, Manfreda G, Remondini D, Cesare AD. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep. 2021;11:3030. https://doi.org/10.1038/s41598-021-82726-y Makki K, Deehan EC, Walter J, Bäckhed F. The Impact of Dietary Fiber on Gut Microbiota in Host Health and Disease. Cell Host Microbe. 2018;23:705–15. https://doi.org/10.1016/j.chom.2018.05.012 Saha B, T RA, Adhikary S, Banerjee A, Radhakrishnan AK, Duttaroy AK, et al. Exploring the Relationship Between Diet, Lifestyle and Gut Microbiome in Colorectal Cancer Development: A Recent Update. Nutr Cancer. 2024;76:789–814. https://doi.org/10.1080/01635581.2024.2367266 Sabir Z, Rosendahl-Riise H, Dierkes J, Dahl H, Hjartåker A. Comparison of dietary intake measured by a web-based FFQ and repeated 24-hour dietary recalls: the Hordaland Health Study. J Nutr Sci. 2022;11:e98. https://doi.org/10.1017/jns.2022.97 Bailey RL. Overview of dietary assessment methods for measuring intakes of foods, beverages, and dietary supplements in research studies. Curr Opin Biotechnol. 2021;70:91–6. https://doi.org/10.1016/j.copbio.2021.02.007 Miao Z, Du W, Xiao C, Su C, Gou W, Shen L, et al. Gut microbiota signatures of long-term and short-term plant-based dietary pattern and cardiometabolic health: a prospective cohort study. BMC Med. 2022;20:204. https://doi.org/10.1186/s12916-022-02402-4 Shim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiology Heal. 2014;36:e2014009. https://doi.org/10.4178/epih/e2014009 Additional Declarations No competing interests reported. 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All biological samples, questionnaires, and assessments are collected at baseline (t₀). Outcomes are derived from baseline measurements and analysed during the data analysis phase (t₁).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9169337/v1/334f45171c20075bda0e5970.png"},{"id":108965443,"identity":"eb982254-31f0-4ba0-88b6-cb58518fb772","added_by":"auto","created_at":"2026-05-11 09:33:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":420463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169337/v1/5e4aba33-17ef-4aae-8347-84e7b5c165f0.pdf"},{"id":105310823,"identity":"bfc24c57-e0b1-49c8-9db2-51fae5d1e861","added_by":"auto","created_at":"2026-03-24 15:12:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23531,"visible":true,"origin":"","legend":"","description":"","filename":"AnnexI.docx","url":"https://assets-eu.researchsquare.com/files/rs-9169337/v1/1baea834b8c5686e269c5381.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of diet and dietary patterns in the composition of gut microbiota (GUTDIET-PT): A multicentre cross-sectional observational study","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cstrong\u003eGut microbiota and its role in health and disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gastrointestinal microbiota is a complex ecosystem composed of trillions of microorganisms that play a fundamental role in maintaining health and preventing disease. This community is largely dominated by two major bacterial phyla: Bacteroidota (Bacteroidetes), including genera such as \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e, and Bacillota (Firmicutes), comprising genera like \u003cem\u003eClostridium, Enterococcus, Lactobacillus\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e. Together, these phyla account for approximately 90% of the gut microbial community. Other phyla include Pseudomonadota (Proteobacteria), Actinomycetota (Actinobacteria), with genera like \u003cem\u003eBifidobacterium\u003c/em\u003e, and Verrucomicrobiota (Verrucomicrobia) [1,2].\u003c/p\u003e\n\u003cp\u003eGut microorganisms synthesise, transform, and metabolise essential nutrients and bioactive molecules, including lipids, bile acids, short-chain fatty acids (SCFAs), amino acids, and vitamins. Emerging evidence suggests that microbiota composition and metabolic activity exert substantial influence over immune regulation, nutritional homeostasis, and metabolic health [3]. Numerous diseases have been associated with gut microbiota imbalances, including gastrointestinal disorders like inflammatory bowel disease and irritable bowel syndrome [4,5], metabolic conditions such as type 2 diabetes Mellitus [6], various types of cancer [7,8], and neurological disorders like Parkinson\u0026apos;s and Alzheimer\u0026rsquo;s disease [9]. Moreover, the gut microbiota may serve not only as a marker of current health status but also as a predictor of long-term health outcomes and longevity [10].\u003c/p\u003e\n\u003cp\u003eAlthough disease-related microbial signatures differ from those found in healthy individuals, there is still uncertainty when defining a \u0026lsquo;healthy\u0026rsquo; microbiome [11]. Certain bacterial species may be associated with either health or disease, and different strains within the same species can exhibit distinct functionalities, which poses a challenge when it comes to associating species with health outcomes [12]. Nevertheless, the recently published consensus from the International Scientific Association for Probiotics and Prebiotics emphasises that gut health cannot be defined solely by microbiome composition, but rather reflects a broader state of normal gastrointestinal function, integrating microbial, host, and clinical factors. [13].\u003c/p\u003e\n\u003cp\u003eInterindividual variation in microbiota composition is shaped by genetics, age, diet, lifestyle, and environmental factors such as physical activity, stress, and medication use [14]. Among these, diet stands out as a modifiable factor with studies showing its major impact in defining the structure and function of the gut microbiota [15]. Understanding the mechanisms through which diet and dietary patterns influence microbial composition and functionality is therefore essential for developing targeted health-promoting strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiet and the gut microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiet exerts a major influence on gut microbiota composition, and the microbiota, in turn, modulates nutrient metabolism and bioavailability, forming a bidirectional relationship [3]. Dietary patterns - rather than isolated nutrients - are key determinants of microbial community structure, reflecting the complex combinations in which foods are habitually consumed. Both short-term dietary changes and long-term dietary patterns can substantially alter microbiota composition, although responses are highly individualised and may not lead to uniform community convergence across individuals [16\u0026ndash;18]. \u003c/p\u003e\n\u003cp\u003eA plant-based diet (PBD), that encompasses vegetarian, vegan, pescatarian, and flexitarian patterns [19], has been associated with lower risks of cardiovascular disease [20], metabolic syndrome [21], type 2 diabetes Mellitus [22], and CRC [23], as well as improved weight management [24]. A recent large-scale, multi-cohort metagenomic study reported that vegan dietary patterns are associated with the enrichment of several species-level genome bins, including known butyrate producers such as \u003cem\u003eButyricicoccus spp., Roseburia hominis\u003c/em\u003e and members of the \u003cem\u003eLachnospiraceae\u003c/em\u003e family that are highly specialised in fibre degradation [25]. \u003c/p\u003e\n\u003cp\u003eThe Mediterranean Diet (MD), often considered a PBD, is widely recognised as a leading dietary model for health promotion and disease prevention [12]. Originating from Mediterranean coastal countries, it emphasises nutrient-rich foods such as fruits, vegetables, whole grains and healthy fats (particularly olive oil), moderate consumption of poultry, fish and dairy products, and low intake of red meat. Its high intake of unprocessed foods contributes to its anti-inflammatory and antioxidant properties, highlighting its role in promoting overall health [26]. Adherence to the MD has been associated with greater microbial diversity and enrichment of beneficial genera, such as \u003cem\u003eBacteroides, Butyricoccus, Lachnoclostridium, Parasutterella, \u003c/em\u003eand \u003cem\u003eLachnospira\u003c/em\u003e [27,28]\u003cem\u003e.\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003eKey components of PBD and MD, particularly dietary fibre, promote the growth of fibre-degrading bacteria that produce SCFAs such as propionate and butyrate, which support gut barrier integrity and intestinal homeostasis, and serve as energy sources for colonocytes and peripheral organs [29,30]. A randomised intervention study showed that adherence to the MD was associated with improved intestinal barrier integrity, reflected by reduced levels of lipopolysaccharide-binding protein and zonulin, alongside increased levels of propionate and butyrate, with these being identified as key mediators of this effect [31]. Polyphenols, abundant in olive oil, wine, fruits, nuts and vegetables, have antioxidant, anti-inflammatory and antibacterial properties and modulate microbiota composition [32]. While polyphenols themselves are poorly absorbed by the human body, they are metabolised by the gut microbiota, providing substrate for bacterial families such as \u003cem\u003eBifidobacteriaceae and Lactobacillaceae\u003c/em\u003e while reducing pathogenic bacteria like \u003cem\u003eEscherichia coli \u003c/em\u003eand \u003cem\u003eClostridium perfringens\u003c/em\u003e [12,33]. Polyphenols also regulate microbial metabolites, including SCFAs, trimethylamine N-oxide (TMAO), and secondary bile acids, by modulating microbial enzyme activity [33]. Polyunsaturated fatty acids, including omega-3, also prominent in the MD, enhance gut epithelial barrier function, reduce inflammation, increase Bacteroidota, and decrease Bacillota-to-Bacteroidota ratio [34,35]. Intervention studies have shown MD-associated increases in \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e and \u003cem\u003eRoseburia faecis \u003c/em\u003eand reduced levels of\u003cem\u003e Ruminococcus gnavus, Collinsella aerofaciens\u003c/em\u003e and \u003cem\u003eRuminococcus torques \u003c/em\u003e[36,37]. \u003c/p\u003e\n\u003cp\u003eThe Western Diet (WD), characterised by high consumption of refined sugars, animal fats, processed meats (especially red meat), refined grains, high-fat dairy products, and fried or pre-packaged foods and low intake of fruits, vegetables, whole grains, meat, fish, nuts, and seeds [38]. This dietary pattern is linked to a higher prevalence of non-communicable diseases, with systematic reviews and meta-analyses showing associations between the WD and increased risk of breast cancer [39], metabolic syndrome [40], gestational diabetes [41], CRC [23], and overall mortality among cancer survivors [42]. Red meat-derived compounds, such as choline and carnitine, can be transformed into trimethylamine and subsequently into TMAO, an organic compound associated with chronic diseases and cardiovascular risk [43]. Furthermore, nitrates and nitrites used in processed meats can be transformed by bacteria into carcinogenic N-nitroso compounds that are associated with a higher risk of GI cancers [44].\u003c/p\u003e\n\u003cp\u003eBeyond assessing adherence to specific dietary patterns, it is also important to evaluate the overall inflammatory potential of the diet [45]. Diets rich in fibre, polyphenols, and unsaturated fatty acids generally exert anti-inflammatory effects and are linked to a more favourable gut microbiome profile. In contrast, dietary patterns high in saturated fats, refined carbohydrates, and processed meats are associated with increased systemic inflammation [46]. Given the bidirectional relationship between inflammation and gut microbiota composition [47], assessing the inflammatory potential of the overall diet may provide additional insight into how dietary patterns influence microbial diversity and functional capacity.\u003c/p\u003e\n\u003cp\u003eIn this study, we will investigate how dietary patterns and their inflammatory potential interplay with the gut microbiota. Understanding these relationships is a crucial step as it helps to shape hypotheses and refine methodologies for future prospective research and can guide the development of public health initiatives, preventive strategies, dietary recommendations, and targeted interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary aim of this study is to investigate how different dietary patterns associate with the composition and functionality of the gut microbiota. Specifically, we will (1) characterise the gut microbiota of the study population, (2) identify prevalent dietary patterns within the study sample, and (3) analyse associations between these dietary patterns, specific dietary constituents, and faecal microbiome profiles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional observational study, conducted at the Gulbenkian Institute for Molecular Medicine, is part of the \u0026ldquo;Improving Colorectal Cancer Early Screening in Portugal: Identification and Validation of Gut Microbiome Biomarkers (GUTBIOME-PT)\u0026rdquo; project (NCT06741293). This project aims to recruit 30,000 participants over a period of 6 years. Recruitment started in November 2023 and is planned to be completed by 2029. From the 30,000 participants recruited for GUTBIOME-PT, we aim to recruit 2500 individuals with a negative colonoscopy result for this study.\u003c/p\u003e\n\u003cp\u003eThe project is approved by the Ethics Committee of the Academic Medicine Center of Lisbon (Ref 111/23). All procedures comply with the principles of the Helsinki Declaration, ensuring informed consent, respect, integrity, privacy and confidentiality for all participants. Participants retain the right to withdraw from the study at any time without penalty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment and eligibility criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants are recruited at partner hospitals, among patients who are referred for a screening colonoscopy and do not have a clinical diagnosis of CRC or a first-degree family history of CRC.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eEligible\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eparticipants are selected from the control group of the GUTBIOME-PT study, consisting of individuals with negative colonoscopy results. Participants who meet all eligibility criteria (Table 1) receive detailed information about the study and are invited to sign the informed consent form.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: Inclusion and exclusion criteria for eligibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e1. To sign the informed consent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e2. Resident in the metropolitan area of Lisbon, Portugal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e3. Participants 40 to 74 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e4. Be referred for a screening colonoscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e1. \u0026lt; 40 or \u0026ge; 75 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e2. Active oncological disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e3.Personal or first-degree family history of CRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e4. Intestinal adenomas removed in the last 24 months\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e5. Diagnosis of inflammatory bowel disease, irritable bowel syndrome or recurrent infection by \u003cem\u003eClostridioides difficile\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e6. Severe cardiovascular or heart diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e7. Severe renal failure requiring hemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e8. Severe lung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e9. Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe overall diagram flow of the study is shown in Fig. 1. and in the SPIRIT figure illustrated in Fig. 2. The SPIRIT checklist is provided as supplementary material.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic, lifestyle, and health-related characteristics: \u003c/strong\u003edata is collected using self-administered questionnaires completed by participants through a GDPR-compliant online platform developed specifically for the GUTBIOME-PT study. The questionnaires are structured into three main sections: sociodemographic characteristics such as education level and economic status, clinical information including any previous or current medical condition, and lifestyle factors such as smoking status, sleep, stress, and physical activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody mass index \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant self-reports their weight and height in a health questionnaire following stool sample collection. Body Mass Index (BMI) is calculated using the formula (BMI = weight (kg) / height\u0026sup2; (m)) and is categorised based on the World Health Organisation\u0026apos;s cutoffs [48].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDietary intake data is collected through telephone interviews conducted by a qualified clinical nutritionist, following validated protocols by the European Food Safety Authority [49] and the Portuguese National Food and Physical Activity Survey [50]. Two non-consecutive 24-hour dietary recalls are performed, recording all foods, beverages, and dietary supplements consumed during the day - from 00h00 to 23h59 - preceding each interview. Each food item is documented with details on the context of consumption (location and time), quantity consumed, and preparation and cooking methods. Common volume measures (e.g., cups, tablespoons, teaspoons, palm) are used to estimate portion sizes. If possible, the brand of the product eaten is recorded. The conversion of food into nutrients is made with nutritional information from the Portuguese Food Composition Table (FCT), available on the PortFIR website [51], version 7.0. For foods not included in the Portuguese FCT, the French [52], and British [53] food composition tables are used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdherence to the MD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD adherence is assessed using the 14-Item Mediterranean Diet Adherence Score developed by the PREvenci\u0026oacute;n con DIeta MEDiterr\u0026aacute;nea (PREDIMED) study authors [54], which includes 12 questions on food consumption and frequency, and two questions on food intake habits that are key principles of the MD. Responses are scored 0 (condition not met) or 1 (condition met), resulting in a score ranging from 0 to 14. Based on this final score, adherence to the MD is categorised as low (score \u0026lt; 5), moderate (score 6-9), or high (score \u0026gt; 10). This questionnaire is applied to participants during the first 24-hour dietary recall. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdherence to a\u003c/strong\u003e \u003cstrong\u003ePBD \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdherence to a PBD is assessed according to the method published by Satija \u003cem\u003eet al\u003c/em\u003e. using a plant-based dietary index (PDI), a healthful PDI (hPDI), and an unhealthful PDI (uPDI) [55]. The individual foods collected through the 24-hour dietary recalls are combined into groups based on animal foods, healthy plant foods, and unhealthy plant foods. Based on the intake levels of these food groups, all food groups are divided into quintiles and assigned separate positive scores (i.e., higher intake receives higher scores, with a score range of 1\u0026ndash;5) or reverse scores (i.e., higher intake receives lower scores, with a score range of 5\u0026ndash;1). For the overall PDI, all plant-based foods are assigned positive scores, whereas animal-based foods are assigned negative scores, and the scores are then summed to obtain an overall PDI score. For the hPDI, positive scores are assigned to healthy plant foods, whereas negative scores are assigned to animal and unhealthy plant foods. For the uPDI, positive scores are assigned to unhealthy plant foods, whereas negative scores are assigned to animal and healthy plant foods. For all three PDIs, higher scores indicate greater adherence to dietary patterns [55,56].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary inflammatory index \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDietary inflammatory potential is assessed using the Dietary Inflammatory Index (DII), as developed by Shivappa \u003cem\u003eet al\u003c/em\u003e. [57]. Dietary intake data derived from the 24-hour dietary recalls are used to calculate individual DII scores. Intakes of available dietary components are standardised to a global reference database by calculating z-scores based on global means and standard deviations. These z-scores are converted to centred percentiles to account for skewed intake distributions. Each centred percentile is then multiplied by a literature-derived inflammatory effect score specific to each dietary component, reflecting its pro- or anti-inflammatory association. To account for total energy intake, an energy-adjusted DII (E-DII) is also calculated by expressing dietary intakes per 1,000 kcal and applying energy-adjusted global reference values. Only dietary components available from the dietary assessment are included in the DII calculation, and missing components are not imputed. The resulting component-specific scores are summed to generate an overall DII and E-DII score, with higher positive values indicating a more pro-inflammatory diet [57].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStool sample characterisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants self‑collect a stool sample using a provided kit (EasySampler\u0026reg; stool collection kit with DNA stabilisation buffer, Invitek), which preserves microbial DNA integrity at the moment of collection. Stool consistency is assessed using the Bristol Stool Scale (BSS) scores [58], which categorises stool into seven types as a proxy for intestinal transit rate [59]. Stool samples are also characterised by colour and recent gastrointestinal symptoms are annotated, such as bloating, cramps or abdominal pain, excessive intestinal gas, mucus in the stool, abnormal stool colour, and presence of blood in the stool. Frequency of bowel movements over the past 5 days is also considered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFaecal DNA extraction and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce collected, the stool samples are transported to the laboratory, where they are subdivided into aliquots and stored at -80\u0026deg;C until further analysis. Samples are processed for DNA extraction within a maximum of 6 months. For faecal microbiome characterisation, samples undergo genomic DNA extraction using the ZymoBIOMICS\u003csup\u003eTM\u003c/sup\u003e 96 MagBead DNA Kit (Zymo Research), compatible with the KingFisher\u0026trade; Flex automatic extractor (Thermo Fisher Scientific). DNA concentrations are measured using a Qubit\u003csup\u003eTM\u003c/sup\u003e 1X dsDNA HS assay kit (Invitrogen\u003csup\u003eTM\u003c/sup\u003e), on a Qubit\u003csup\u003eTM\u003c/sup\u003e Flex Fluorometer (Life Technologies). The composition and genetic content of the faecal microbiota are analysed through shotgun metagenomic sequencing, performed on an Illumina platform (NovaSeq X Plus), with a minimum of 3 million reads per sample. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant withdrawal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with the Declaration of Helsinki, participants are informed that they may withdraw from the study at any time. Reasons for withdrawal are reported in the source documents and on the case report form. Samples from participants who withdraw are preserved and may still be analysed, as long as the participant does not request the destruction of their data. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMissing Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf a sample is missing, associated data (e.g., diet-related questionnaires) is excluded from the analysis, and the participant is considered withdrawn to maintain data integrity, as biological samples are essential for the study. Additionally, participants who do not complete mandatory questionnaires are considered excluded from the study, and their sample is not considered for analysis. If the participant completes only one 24-hour dietary recall, this data is included in the analysis. Although multiple interviews are preferred for accuracy, a single interview can still provide relevant insights into dietary habits [49].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData codification \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipant data is confidential and identified only by a unique participant code (\u0026quot;Participant ID\u0026quot;) on both faecal samples, questionnaires, and the electronic database. Data from questionnaires is recorded on a secure online platform, with sample results stored in a separate database linked through the participant\u0026apos;s code to ensure participant anonymity. Personal data is processed based on the participant\u0026rsquo;s explicit consent, strictly for scientific research, study-related communication, and compliance with legal requirements and regulatory obligations.\u003cbr\u003e\u003cstrong\u003eData Storage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePersonal data is stored with restricted access in compliance with the General Data Protection Regulation and relevant legislation. All data remains within the originating institution in compliance with the data management plan, which outlines protocols for data handling, storage, and sharing. To increase accessibility, pseudonymisation is employed to make the data more widely available. Due to intellectual property constraints, data may not be fully open access. Data and document ownership are clearly assigned, and a designated individual will manage data transfer at project completion. Data will be archived in cold storage for long-term preservation. This approach will help ensure the data remains comprehensible and reusable over time.\u003c/p\u003e\n\u003cp\u003ePersonal information is processed in a pseudonymised format, with access to decryption keys restricted to authorised personnel. This project is being conducted in collaboration with multiple institutions under a formal collaboration agreement that outlines data access permissions, with ongoing updates as needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size estimation was informed by power analyses using the Shannon Diversity Index as the primary outcome. Based on the effect size reported by Malinowska \u003cem\u003eet al\u003c/em\u003e. [60] (mean difference \u0026asymp; 0.08 units; Cohen\u0026rsquo;s d \u0026asymp; 0.17), balanced two‑group comparisons achieved \u0026ge;90% power with 1500-2000 participants. However, we acknowledge that power will be reduced under unbalanced group allocations.\u003c/p\u003e\n\u003cp\u003eTo ensure adequate statistical power while accounting for attrition, we applied a 20% anticipated drop-out rate. Therefore, to retain an effective sample size of 2000 participants, the study will aim to recruit approximately:\u003c/p\u003e\n\u003cp\u003e2000 \u0026divide; 0.80 = 2,500 participants\u003c/p\u003e\n\u003cp\u003eThus, the target recruitment number is 2,500 participants, allowing sufficient power for primary comparisons even with expected loss to follow-up or incomplete data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis is performed using RStudio (R Foundation for Statistical Computing) and Python, with p \u0026lt; 0.05 considered statistically significant. R is used for statistical testing, data analysis, and visualisation, while Python supports advanced data analysis and statistical modelling to enhance the robustness of the findings and enable in-depth biological interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobiome Analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe faecal DNA obtained from participants undergoes shotgun metagenomic sequencing [61], generating approximately 3 to 4 gigabytes of data per sample. Paired-end sequencing is performed, and the resulting raw sequences are processed to ensure high-quality data. Fastp is employed to trim and remove low-quality reads [62], and bowtie2 is used to eliminate host contamination by aligning the sample reads to the human genome reference (GRCh38) [63].\u003c/p\u003e\n\u003cp\u003eTo quantify the abundance of microbial taxa, a hybrid approach combining k-mer analysis and mapping techniques is applied to the preprocessed sequences, utilising publicly available genome databases such as GTDB [64] and UHGG [65] for reference support. The functional potential of the microbiomes is inferred by directly mapping the metagenomic data to functional databases such as Uniprot [66], KEGG [67], or CAZy [68] and performing gene annotation and characterisation of the assembled metagenomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis focuses on evaluating the associations between dietary patterns and gut microbiota composition and function. Major dietary patterns present in the study sample will be identified and characterised using PCA with varimax rotation, \u003cem\u003ea posteriori\u003c/em\u003e approach (data-driven) that is a variable reduction procedure based on correlation or covariance matrices of the original variables, creating linear combinations such as patterns [69]. The individual foods collected through the 24-hour dietary recalls will be combined into groups based on their nutritional components- similarity and culinary uses, and these groups will serve as variables for the PCA, as previously described [70]. Alternatively, clustering techniques (e.g., k-means) may be employed to classify participants into distinct dietary groups.\u003c/p\u003e\n\u003cp\u003eMicrobial alpha diversity (within-sample diversity) will be assessed using Shannon, Simpson, and observed richness indices, while beta diversity (between-sample differences) will be calculated using Bray-Curtis dissimilarity and weighted/unweighted UniFrac metrics. Differences in alpha diversity between dietary groups will be evaluated using the Kruskal-Wallis test, and beta diversity will be compared using PERMANOVA (Permutational Multivariate Analysis of Variance), with visualisation through Principal Coordinate Analysis (PCoA) plots.\u003c/p\u003e\n\u003cp\u003eDifferential abundance analysis will be conducted to identify taxa and functional pathways associated with specific dietary patterns while controlling for multiple comparisons with the Benjamini-Hochberg correction. Correlation analyses (Spearman or Pearson) will be performed to explore the relationships between dietary intake (e.g., macronutrient composition) and the relative abundance of microbial taxa and functional features. Multivariable models, including linear or logistic regression, will adjust for potential confounders such as age, sex, BMI, and physical activity to ensure robust associations. Additionally, machine learning approaches, such as Random Forests, will be applied to identify key microbial taxa or functions predictive of dietary patterns. Statistical significance will be set at p \u0026lt; 0.05 unless otherwise stated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential outcomes and clinical impact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first large-scale observational study to examine how distinct dietary patterns relate to gut microbiota composition and function in the Portuguese population using shotgun metagenomic sequencing. The sample size and methodological rigour provide a robust foundation for characterising diet\u0026ndash;microbiota interactions and generating evidence that may inform future research, nutritional guidance, and public health strategies.\u003c/p\u003e\n\u003cp\u003eShotgun metagenomic sequencing offers higher taxonomic and functional resolution than 16S rRNA amplicon sequencing, allowing the detection of low-abundance taxa and enabling detailed functional profiling [71,72]. This methodology enhances the capacity to identify structural variation and metabolic pathways within the microbiome [61]. Combined with detailed dietary information obtained through structured nutritional interviews, this approach enables a comprehensive assessment of the relationship between dietary patterns and microbiota composition and function.\u003c/p\u003e\n\u003cp\u003ePrevious studies have demonstrated that specific nutrients, such as fibre or saturated fat, and foods, such as red meat, are associated with distinct microbiota compositions [43,73]. However, focusing only on individual nutrients or foods may fail to explain their overall effect on health, since it does not take into account the interaction between nutrients and foods. Studying dietary patterns provides a more holistic and comprehensive approach. Research has analysed the associations between dietary patterns - such as MD, PBD and WD - and microbiota composition, but these studies often focus on specific diseases, such as cancer or metabolic disorders [3,74]. By contrast, the present study focuses on healthy adults, allowing us to characterise microbiota composition in the absence of disease and to identify dietary behaviours that support microbial diversity and functional capacity. This approach contributes to a clearer understanding of microbiome characteristics associated with health and provides a foundational framework for identifying dietary patterns that promote microbial diversity and functional capacity. \u003c/p\u003e\n\u003cp\u003eFurthermore, large population-based metagenomic studies remain limited in Mediterranean countries. By examining the Portuguese population within its specific cultural and dietary context, this study helps address a national knowledge gap while also expanding the evidence base for microbiome research. This contributes to a broader and more nuanced understanding of microbiota variability across Mediterranean populations.\u003c/p\u003e\n\u003cp\u003eDietary intake will be assessed using two non‑consecutive 24‑hour dietary recalls, a validated method that captures detailed information on consumed foods and beverages [49]. Compared with food frequency questionnaires, which may lack precision and restrict responses to predefined items, 24‑hour recalls allow for more accurate, flexible assessment of dietary behaviours, including the detection of non‑listed foods [75,76]. This short-term resolution is particularly relevant given the rapid responsiveness of the gut microbiota to recent dietary intake, which long-term dietary tools may not accurately capture [77]. Although recall-based methods are subject to reporting bias [75,78], this limitation will be mitigated through validated protocols [49], structured interviews conducted by trained nutritionists, and statistical adjustments for potential confounders. While some participants may provide only a single recall, the large sample size of the study is expected to reduce variability and support reliable dietary characterisation.\u003c/p\u003e\n\u003cp\u003eAltogether, this study is expected to generate valuable insights into the complex interactions between diet and gut microbiota. By clarifying how dietary patterns relate to microbial composition and function, the findings may inform evidence‑based public health and nutritional strategies aimed at promoting gut health. In the longer term, this work has the potential to support precision nutrition approaches tailored to individual dietary habits and microbiota profiles, ultimately enhancing the effectiveness of dietary interventions designed to improve health and well‑being.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI - Body Mass Index\u003c/p\u003e\n\u003cp\u003eBSS - Bristol Stool Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRC - Colorectal Cancer\u003c/p\u003e\n\u003cp\u003eDII - Dietary Inflammatory Index\u003c/p\u003e\n\u003cp\u003ehPDI - Healthful Plant-Based Dietary Index\u003c/p\u003e\n\u003cp\u003eMD - Mediterranean Diet\u003c/p\u003e\n\u003cp\u003ePBD - Plant-Based Diet\u003c/p\u003e\n\u003cp\u003ePDI \u0026ndash;\u0026nbsp;Plant-Based Dietary Index\u003c/p\u003e\n\u003cp\u003ePREDIMED - PREvenci\u0026oacute;n con DIeta MEDiterr\u0026aacute;nea\u003c/p\u003e\n\u003cp\u003eSCFA - Short-Chain Fatty Acids\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTMAO - Trimethylamine N-oxide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003euPDI - Unhealthful Plant-Based Dietary Index\u003c/p\u003e\n\u003cp\u003eWD - Western Diet\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was approved by the Ethics Committee of the Academic Medicine Center of Lisbon (Ref 111/23) in November 2023. All procedures comply with the principles of the Helsinki Declaration, ensuring informed consent, respect, integrity, privacy and confidentiality for all participants. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnless specified by local laws or regulations, the Sponsor will be responsible for ownership of the data, results, reports, conclusions, or findings related to this study. Consequently, the Sponsor reserves the right to ownership and use of the data from this study for the purpose described in the project. Access to the data obtained may be granted to other qualified researchers proposing valid scientific analyses, always respecting the confidentiality of the data and the anonymity of the participants. Information regarding the identification of participants will never be shared, respecting the data confidentiality mentioned above.\u003c/p\u003e\n\n\u003ch4\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is funded by GIMM-CARE. GIMM-CARE is funded by the European Union under grant agreement No. 101060102. GIMM-CARE is co-funded by the Portuguese Government, the National Foundation for Science and Technology (FCT), ARICA \u0026ndash; Investimentos, Participa\u0026ccedil;\u0026otilde;es e Gest\u0026atilde;o, Jer\u0026oacute;nimo Martins, the Gulbenkian Institute for Molecular Medicine and CAML - Lisbon Academic Medical Centre. ASA is supported by a grant from Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e Tecnologia (2021.02791.CEECIND). AS is supported by a grant from Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e Tecnologia (2025.02293.BD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCSG, IS and ASA contributed to the conception and design of the study. MMM and JS drafted the manuscript, and all authors critically read and revised it. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to sincerely thank the Mission Lab Team for handling and processing the faecal samples, and the Biobank at GIMM-Care, where all biological samples will be stored. We also thank the Gastroenterology and General Medicine Teams at our partner hospitals (Hospital da Luz, CUF Tejo, and CUF Descobertas) for recruiting study participants, as well as all study coordinators for participant registration. We are grateful to the Advanced Data Analysis team from the The Digital Technologies Unit at GIMM for their support with the sample size calculation, and to the members of the Microbiome in Health and Disease Translational Laboratory for their critical input on the manuscript. Finally, we extend our sincere appreciation to all participants of the GUTBIOME-PT study who kindly agreed to take part in this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59\u0026ndash;65. https://doi.org/10.1038/nature08821\u003c/li\u003e\n\u003cli\u003eTap J, Mondot S, Levenez F, Pelletier E, Caron C, Furet J, et al. Towards the human intestinal microbiota phylogenetic core. Environ Microbiol. 2009;11:2574\u0026ndash;84. https://doi.org/10.1111/j.1462-2920.2009.01982.x\u003c/li\u003e\n\u003cli\u003eBreuninger TA, Wawro N, Breuninger J, Reitmeier S, Clavel T, Six-Merker J, et al. Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation. Microbiome. 2021;9:61. https://doi.org/10.1186/s40168-020-00969-9\u003c/li\u003e\n\u003cli\u003eIliev ID, Ananthakrishnan AN, Guo C-J. Microbiota in inflammatory bowel disease: mechanisms of disease and therapeutic opportunities. Nat Rev Microbiol. 2025;23:509\u0026ndash;24. https://doi.org/10.1038/s41579-025-01163-0\u003c/li\u003e\n\u003cli\u003eDuan R, Zhu S, Wang B, Duan L. Alterations of Gut Microbiota in Patients With Irritable Bowel Syndrome Based on 16S rRNA-Targeted Sequencing: A Systematic Review. Clin Transl Gastroenterol. 2019;10:e00012. https://doi.org/10.14309/ctg.0000000000000012\u003c/li\u003e\n\u003cli\u003eMei Z, Wang F, Bhosle A, Dong D, Mehta R, Ghazi A, et al. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes. Nat Med. 2024;30:2265\u0026ndash;76. https://doi.org/10.1038/s41591-024-03067-7\u003c/li\u003e\n\u003cli\u003eFernandez E, Wargo JA, Helmink BA. The Microbiome and Cancer. JAMA. 2025;333:2188\u0026ndash;96. https://doi.org/10.1001/jama.2025.2191\u003c/li\u003e\n\u003cli\u003eNobels A, Marcke C van, Jordan BF, Hul MV, Cani PD. The gut microbiome and cancer: from tumorigenesis to therapy. Nat Metab. 2025;7:895\u0026ndash;917. https://doi.org/10.1038/s42255-025-01287-w\u003c/li\u003e\n\u003cli\u003eCryan JF, O\u0026rsquo;Riordan KJ, Sandhu K, Peterson V, Dinan TG. The gut microbiome in neurological disorders. Lancet Neurol. 2020;19:179\u0026ndash;94. https://doi.org/10.1016/s1474-4422(19)30356-4\u003c/li\u003e\n\u003cli\u003eJoos R, Boucher K, Lavelle A, Arumugam M, Blaser MJ, Claesson MJ, et al. Examining the healthy human microbiome concept. Nat Rev Microbiol. 2024;1\u0026ndash;14. https://doi.org/10.1038/s41579-024-01107-0\u003c/li\u003e\n\u003cli\u003eGacesa R, Kurilshikov A, Vila AV, Sinha T, Klaassen MAY, Bolte LA, et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature. 2022;604:732\u0026ndash;9. https://doi.org/10.1038/s41586-022-04567-7\u003c/li\u003e\n\u003cli\u003eRoss FC, Patangia D, Grimaud G, Lavelle A, Dempsey EM, Ross RP, et al. The interplay between diet and the gut microbiome: implications for health and disease. Nat Rev Microbiol. 2024;1\u0026ndash;16. https://doi.org/10.1038/s41579-024-01068-4\u003c/li\u003e\n\u003cli\u003eMarco ML, Cunningham M, Bischoff SC, Clarke G, Delzenne N, Lewis JD, et al. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of gut health. Nat Rev Gastroenterol Hepatol. 2026;1\u0026ndash;17. https://doi.org/10.1038/s41575-026-01176-x\u003c/li\u003e\n\u003cli\u003eMousa WK, Chehadeh F, Husband S. Recent Advances in Understanding the Structure and Function of the Human Microbiome. Front Microbiol. 2022;13:825338. https://doi.org/10.3389/fmicb.2022.825338\u003c/li\u003e\n\u003cli\u003eZmora N, Suez J, Elinav E. You are what you eat: diet, health and the gut microbiota. Nat Rev Gastroenterol Hepatol. 2019;16:35\u0026ndash;56. https://doi.org/10.1038/s41575-018-0061-2\u003c/li\u003e\n\u003cli\u003eRinninella E, Tohumcu E, Raoul P, Fiorani M, Cintoni M, Mele MC, et al. The role of diet in shaping human gut microbiota. Best Pr Res Clin Gastroenterol. 2023;62:101828. https://doi.org/10.1016/j.bpg.2023.101828\u003c/li\u003e\n\u003cli\u003eSenghor B, Sokhna C, Ruimy R, Lagier J-C. Gut microbiota diversity according to dietary habits and geographical provenance. Hum Microbiome J. 2018;7:1\u0026ndash;9. https://doi.org/10.1016/j.humic.2018.01.001\u003c/li\u003e\n\u003cli\u003eVermeulen A, Bootsma E, Proost S, Vieira-Silva S, Kathagen G, V\u0026aacute;zquez-Castellanos JF, et al. Dietary convergence induces individual responses in faecal microbiome composition. eGastroenterology. 2025;3:e100161. https://doi.org/10.1136/egastro-2024-100161\u003c/li\u003e\n\u003cli\u003eZhao Y, Zhan J, Wang Y, Wang D. The Relationship Between Plant-Based Diet and Risk of Digestive System Cancers: A Meta-Analysis Based on 3,059,009 Subjects. Front Public Heal. 2022;10:892153. https://doi.org/10.3389/fpubh.2022.892153\u003c/li\u003e\n\u003cli\u003eQuek J, Lim G, Lim WH, Ng CH, So WZ, Toh J, et al. The Association of Plant-Based Diet With Cardiovascular Disease and Mortality: A Meta-Analysis and Systematic Review of Prospect Cohort Studies. Front Cardiovasc Med. 2021;8:756810. https://doi.org/10.3389/fcvm.2021.756810\u003c/li\u003e\n\u003cli\u003eNikparast A, Etesami E, Rahmani J, Rafiei N, Ghanavati M. The association between plant-based diet indices and metabolic syndrome: a systematic review and dose\u0026ndash;response meta-analysis. Front Nutr. 2024;10:1305755. https://doi.org/10.3389/fnut.2023.1305755\u003c/li\u003e\n\u003cli\u003eQian F, Liu G, Hu FB, Bhupathiraju SN, Sun Q. Association Between Plant-Based Dietary Patterns and Risk of Type 2 Diabetes. JAMA Intern Med. 2019;179:1335\u0026ndash;44. https://doi.org/10.1001/jamainternmed.2019.2195\u003c/li\u003e\n\u003cli\u003eGarcia-Larsen V, Morton V, Norat T, Moreira A, Potts JF, Reeves T, et al. Dietary patterns derived from principal component analysis (PCA) and risk of colorectal cancer: a systematic review and meta-analysis. Eur J Clin Nutr. 2019;73:366\u0026ndash;86. https://doi.org/10.1038/s41430-018-0234-7\u003c/li\u003e\n\u003cli\u003eTran E, Dale HF, Jensen C, Lied GA. Effects of Plant-Based Diets on Weight Status: A Systematic Review. Diabetes, Metab Syndr Obes: Targets Ther. 2020;13:3433\u0026ndash;48. https://doi.org/10.2147/dmso.s272802\u003c/li\u003e\n\u003cli\u003eFackelmann G, Manghi P, Carlino N, Heidrich V, Piccinno G, Ricci L, et al. Gut microbiome signatures of vegan, vegetarian and omnivore diets and associated health outcomes across 21,561 individuals. Nat Microbiol. 2025;10:41\u0026ndash;52. https://doi.org/10.1038/s41564-024-01870-z\u003c/li\u003e\n\u003cli\u003eTrichopoulou A, Mart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Tong TY, Forouhi NG, Khandelwal S, Prabhakaran D, et al. Definitions and potential health benefits of the Mediterranean diet: views from experts around the world. BMC Med. 2014;12:112. https://doi.org/10.1186/1741-7015-12-112\u003c/li\u003e\n\u003cli\u003eBourdeau-Julien I, Castonguay-Paradis S, Rochefort G, Perron J, Lamarche B, Flamand N, et al. The diet rapidly and differentially affects the gut microbiota and host lipid mediators in a healthy population. Microbiome. 2023;11:26. https://doi.org/10.1186/s40168-023-01469-2\u003c/li\u003e\n\u003cli\u003eArmet AM, Deehan EC, O\u0026rsquo;Sullivan AF, Mota JF, Field CJ, Prado CM, et al. Rethinking healthy eating in light of the gut microbiome. Cell Host Microbe. 2022;30:764\u0026ndash;85. https://doi.org/10.1016/j.chom.2022.04.016\u003c/li\u003e\n\u003cli\u003eWang DD, Nguyen LH, Li Y, Yan Y, Ma W, Rinott E, et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat Med. 2021;27:333\u0026ndash;43. https://doi.org/10.1038/s41591-020-01223-3\u003c/li\u003e\n\u003cli\u003eBesten G den, Eunen K van, Groen AK, Venema K, Reijngoud D-J, Bakker BM. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res. 2013;54:2325\u0026ndash;40. https://doi.org/10.1194/jlr.r036012\u003c/li\u003e\n\u003cli\u003eSeethaler B, Nguyen NK, Basrai M, Kiechle M, Walter J, Delzenne NM, et al. Short-chain fatty acids are key mediators of the favorable effects of the Mediterranean diet on intestinal barrier integrity: data from the randomized controlled LIBRE trial. Am J Clin Nutr. 2022;116:928\u0026ndash;42. https://doi.org/10.1093/ajcn/nqac175\u003c/li\u003e\n\u003cli\u003eCatalkaya G, Venema K, Lucini L, Rocchetti G, Delmas D, Daglia M, et al. Interaction of dietary polyphenols and gut microbiota: Microbial metabolism of polyphenols, influence on the gut microbiota, and implications on host health. Food Front. 2020;1:109\u0026ndash;33. https://doi.org/10.1002/fft2.25\u003c/li\u003e\n\u003cli\u003eRudrapal M, Oliveira AM de, Singh RP. Dietary polyphenols maintain human health through modulation of gut microbiota. Front Pharmacol. 2025;16:1710088. https://doi.org/10.3389/fphar.2025.1710088\u003c/li\u003e\n\u003cli\u003eLiu H, Li X, Zhu Y, Huang Y, Zhang Q, Lin S, et al. Effect of Plant-Derived n-3 Polyunsaturated Fatty Acids on Blood Lipids and Gut Microbiota: A Double-Blind Randomized Controlled Trial. Front Nutr. 2022;9:830960. https://doi.org/10.3389/fnut.2022.830960\u003c/li\u003e\n\u003cli\u003eTelle-Hansen VH, Gaundal L, Bastani N, Rud I, Byfuglien MG, Gj\u0026oslash;vaag T, et al. Replacing saturated fatty acids with polyunsaturated fatty acids increases the abundance of Lachnospiraceae and is associated with reduced total cholesterol levels\u0026mdash;a randomized controlled trial in healthy individuals. Lipids Heal Dis. 2022;21:92. https://doi.org/10.1186/s12944-022-01702-1\u003c/li\u003e\n\u003cli\u003eGhosh TS, Rampelli S, Jeffery IB, Santoro A, Neto M, Capri M, et al. Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries. Gut. 2020;69:1218\u0026ndash;28. https://doi.org/10.1136/gutjnl-2019-319654\u003c/li\u003e\n\u003cli\u003eMeslier V, Laiola M, Roager HM, Filippis FD, Roume H, Quinquis B, et al. 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;69:1258\u0026ndash;68. https://doi.org/10.1136/gutjnl-2019-320438\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Montero C, Fraile-Mart\u0026iacute;nez O, G\u0026oacute;mez-Lahoz AM, Pekarek L, Castellanos AJ, Noguerales-Fraguas F, et al. Nutritional Components in Western Diet Versus Mediterranean Diet at the Gut Microbiota\u0026ndash;Immune System Interplay. Implications for Health and Disease. Nutrients. 2021;13:699. https://doi.org/10.3390/nu13020699\u003c/li\u003e\n\u003cli\u003eXiao Y, Xia J, Li L, Ke Y, Cheng J, Xie Y, et al. Associations between dietary patterns and the risk of breast cancer: a systematic review and meta-analysis of observational studies. Breast Cancer Res. 2019;21:16. https://doi.org/10.1186/s13058-019-1096-1\u003c/li\u003e\n\u003cli\u003eUshula TW, Mamun A, Darssan D, Wang WYS, Williams GM, Whiting SJ, et al. Dietary patterns and the risks of metabolic syndrome and insulin resistance among young adults: Evidence from a longitudinal study. Clin Nutr. 2022;41:1523\u0026ndash;31. https://doi.org/10.1016/j.clnu.2022.05.006\u003c/li\u003e\n\u003cli\u003eQuan W, Zeng M, Jiao Y, Li Y, Xue C, Liu G, et al. Western Dietary Patterns, Foods, and Risk of Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis of Prospective Cohort Studies. Adv Nutr. 2021;12:1353\u0026ndash;64. https://doi.org/10.1093/advances/nmaa184\u003c/li\u003e\n\u003cli\u003eSchwedhelm C, Boeing H, Hoffmann G, Aleksandrova K, Schwingshackl L. Effect of diet on mortality and cancer recurrence among cancer survivors: a systematic review and meta-analysis of cohort studies. Nutr Rev. 2016;74:737\u0026ndash;48. https://doi.org/10.1093/nutrit/nuw045\u003c/li\u003e\n\u003cli\u003eSeverino A, Tohumcu E, Tamai L, Dargenio P, Porcari S, Rondinella D, et al. The microbiome-driven impact of western diet in the development of noncommunicable chronic disorders. Best Pr Res Clin Gastroenterol. 2024;72:101923. https://doi.org/10.1016/j.bpg.2024.101923\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang Y, Jia J, Peng H, Qian Q, Pan Z, et al. Nitrite and nitrate in meat processing: Functions and alternatives. Curr Res Food Sci. 2023;6:100470. https://doi.org/10.1016/j.crfs.2023.100470\u003c/li\u003e\n\u003cli\u003eV\u0026aacute;zquez-Lorente H, Hern\u0026aacute;ndez-Cacho A, Garc\u0026iacute;a-Gavil\u0026aacute;n JF, Li J, Ruiz-Canela M, Belzer C, et al. Inflammatory dietary potential and gut microbiota in older adults with overweight or obesity and metabolic syndrome. Food Res Int. 2025;221:117263. https://doi.org/10.1016/j.foodres.2025.117263\u003c/li\u003e\n\u003cli\u003eMirhosseini SM, Mahdavi A, Yarmohammadi H, Razavi A, Rezaei M, Soltanipur M, et al. What is the link between the dietary inflammatory index and the gut microbiome? A systematic review. Eur J Nutr. 2024;63:2407\u0026ndash;19. https://doi.org/10.1007/s00394-024-03470-3\u003c/li\u003e\n\u003cli\u003eHul MV, Cani PD, Petitfils C, Vos WMD, Tilg H, El-Omar EM. What defines a healthy gut microbiome? Gut. 2024;73:1893\u0026ndash;908. https://doi.org/10.1136/gutjnl-2024-333378\u003c/li\u003e\n\u003cli\u003eCB W, A J. BMI Classification Percentile and Cut Off Points [Internet]. Bethesda (MD): National Center for Biotechnology Information; 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK541070/. Accessed November 07, 2024.\u003c/li\u003e\n\u003cli\u003eAuthority EFS. General principles for the collection of national food consumption data in the view of a pan‐European dietary survey. EFSA J. 2009;7:1435. https://doi.org/10.2903/j.efsa.2009.1435\u003c/li\u003e\n\u003cli\u003eLopes C, Torres D, Oliveira A, Severo M, Alarc\u0026atilde;o V, Guiomar S, et al. Inqu\u0026eacute;rito Alimentar Nacional e de Atividade F\u0026iacute;sica, IAN-AF 2015- 2016: Relat\u0026oacute;rio metodol\u0026oacute;gico. Universidade do Porto, 2017. ISBN: 978-989-746-180-4. Dispon\u0026iacute;vel em: www.ian-af.up.pt. Acessed November 7,2024. 2017;\u003c/li\u003e\n\u003cli\u003eInstituto Nacional de Sa\u0026uacute;de Doutor Ricardo Jorge (INSA). Dispon\u0026iacute;vel nova vers\u0026atilde;o da Tabela de Composi\u0026ccedil;\u0026atilde;o de Alimentos [Internet]. Lisboa: INSA; 2023. Available from: https://www.insa.min-saude.pt/disponivel-nova-versao-da-tabela-de-composicao-de-alimentos/. Accessed October 15, 2024.\u003c/li\u003e\n\u003cli\u003eANSES-CIQUAL. CIQUAL Food Composition Table [Internet]. Maisons-Alfort: French Agency for Food, Environmental and Occupational Health \u0026amp; Safety; 2020. Available from: https://ciqual.anses.fr/. Accessed October 15, 2024.\u003c/li\u003e\n\u003cli\u003ePublic Health England (PHE). McCance and Widdowson\u0026rsquo;s The Composition of Foods Integrated Dataset (CoFID) [Internet]. London: PHE; 2021. Available from: https://www.gov.uk/government/publications/composition-of-foods-integrated-dataset-cofid. Accessed October 15, 2024.\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Garc\u0026iacute;a-Arellano A, Toledo E, Salas-Salvad\u0026oacute; J, Buil-Cosiales P, Corella D, et al. A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial. PLoS ONE. 2012;7:e43134. https://doi.org/10.1371/journal.pone.0043134\u003c/li\u003e\n\u003cli\u003eSatija A, Bhupathiraju SN, Rimm EB, Spiegelman D, Chiuve SE, Borgi L, et al. Plant-Based Dietary Patterns and Incidence of Type 2 Diabetes in US Men and Women: Results from Three Prospective Cohort Studies. PLoS Med. 2016;13:e1002039. https://doi.org/10.1371/journal.pmed.1002039\u003c/li\u003e\n\u003cli\u003eLi X, Li M, Cheng J, Guan S, Hou L, Zu S, et al. Association of healthy and unhealthy plant-based diets with telomere length. Clin Nutr. 2024;43:1694\u0026ndash;701. https://doi.org/10.1016/j.clnu.2024.06.004\u003c/li\u003e\n\u003cli\u003eShivappa N, Steck SE, Hurley TG, Hussey JR, H\u0026eacute;bert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Heal Nutr. 2014;17:1689\u0026ndash;96. https://doi.org/10.1017/s1368980013002115\u003c/li\u003e\n\u003cli\u003eTigchelaar EF, Bonder MJ, Jankipersadsing SA, Fu J, Wijmenga C, Zhernakova A. Gut microbiota composition associated with stool consistency. Gut. 2016;65:540. https://doi.org/10.1136/gutjnl-2015-310328\u003c/li\u003e\n\u003cli\u003eVandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016;65:57. https://doi.org/10.1136/gutjnl-2015-309618\u003c/li\u003e\n\u003cli\u003eMalinowska AM, Kok DE, Steegenga WT, Hooiveld GJEJ, Chmurzynska A. Human gut microbiota composition and its predicted functional properties in people with western and healthy dietary patterns. Eur J Nutr. 2022;61:3887\u0026ndash;903. https://doi.org/10.1007/s00394-022-02928-6\u003c/li\u003e\n\u003cli\u003eQuince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833\u0026ndash;44. https://doi.org/10.1038/nbt.3935\u003c/li\u003e\n\u003cli\u003eChen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884\u0026ndash;90. https://doi.org/10.1093/bioinformatics/bty560\u003c/li\u003e\n\u003cli\u003eLangmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357\u0026ndash;9. https://doi.org/10.1038/nmeth.1923\u003c/li\u003e\n\u003cli\u003eParks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil P-A, Hugenholtz P. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 2021;50:D785\u0026ndash;94. https://doi.org/10.1093/nar/gkab776\u003c/li\u003e\n\u003cli\u003eAlmeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, Shi ZJ, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol. 2021;39:105\u0026ndash;14. https://doi.org/10.1038/s41587-020-0603-3\u003c/li\u003e\n\u003cli\u003eConsortium TU. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47:D506\u0026ndash;15. https://doi.org/10.1093/nar/gky1049\u003c/li\u003e\n\u003cli\u003eKanehisa M. \u0026lsquo;In Silico\u0026rsquo; Simulation of Biological Processes. Novartis Found Symp. 2016;91\u0026ndash;103. https://doi.org/10.1002/0470857897.ch8\u003c/li\u003e\n\u003cli\u003eCantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233\u0026ndash;8. https://doi.org/10.1093/nar/gkn663\u003c/li\u003e\n\u003cli\u003eVelho S, Mo\u0026ccedil;o S, Ferreira A, Cruz R, Agostinho L, Cabral MS, et al. Dietary patterns and their relationships to sarcopenia in Portuguese patients with gastrointestinal cancer: An exploratory study. Nutrition. 2019;63:193\u0026ndash;9. https://doi.org/10.1016/j.nut.2019.01.014\u003c/li\u003e\n\u003cli\u003eSantos R de O, Gorgulho BM, Castro MA de, Fisberg RM, Marchioni DM, Baltar VT. Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application. Rev Bras Epidemiologia. 2019;22:e190041. https://doi.org/10.1590/1980-549720190041\u003c/li\u003e\n\u003cli\u003eBars-Cortina D, Ramon E, Rius-Sansalvador B, Guin\u0026oacute; E, Garcia-Serrano A, Mach N, et al. Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota. BMC Genom. 2024;25:730. https://doi.org/10.1186/s12864-024-10621-7\u003c/li\u003e\n\u003cli\u003eDurazzi F, Sala C, Castellani G, Manfreda G, Remondini D, Cesare AD. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep. 2021;11:3030. https://doi.org/10.1038/s41598-021-82726-y\u003c/li\u003e\n\u003cli\u003eMakki K, Deehan EC, Walter J, B\u0026auml;ckhed F. The Impact of Dietary Fiber on Gut Microbiota in Host Health and Disease. Cell Host Microbe. 2018;23:705\u0026ndash;15. https://doi.org/10.1016/j.chom.2018.05.012\u003c/li\u003e\n\u003cli\u003eSaha B, T RA, Adhikary S, Banerjee A, Radhakrishnan AK, Duttaroy AK, et al. Exploring the Relationship Between Diet, Lifestyle and Gut Microbiome in Colorectal Cancer Development: A Recent Update. Nutr Cancer. 2024;76:789\u0026ndash;814. https://doi.org/10.1080/01635581.2024.2367266\u003c/li\u003e\n\u003cli\u003eSabir Z, Rosendahl-Riise H, Dierkes J, Dahl H, Hjart\u0026aring;ker A. Comparison of dietary intake measured by a web-based FFQ and repeated 24-hour dietary recalls: the Hordaland Health Study. J Nutr Sci. 2022;11:e98. https://doi.org/10.1017/jns.2022.97\u003c/li\u003e\n\u003cli\u003eBailey RL. Overview of dietary assessment methods for measuring intakes of foods, beverages, and dietary supplements in research studies. Curr Opin Biotechnol. 2021;70:91\u0026ndash;6. https://doi.org/10.1016/j.copbio.2021.02.007\u003c/li\u003e\n\u003cli\u003eMiao Z, Du W, Xiao C, Su C, Gou W, Shen L, et al. Gut microbiota signatures of long-term and short-term plant-based dietary pattern and cardiometabolic health: a prospective cohort study. BMC Med. 2022;20:204. https://doi.org/10.1186/s12916-022-02402-4\u003c/li\u003e\n\u003cli\u003eShim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiology Heal. 2014;36:e2014009. https://doi.org/10.4178/epih/e2014009\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut Microbiota, Dietary Patterns, Mediterranean Diet, Western Diet, Plant-based Diet, Dietary Inflammatory Index","lastPublishedDoi":"10.21203/rs.3.rs-9169337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe gut microbiome plays a fundamental role in human health, influencing immune, metabolic, and nutritional functions. Diet is a key factor in shaping microbiota composition, with dietary patterns like the Mediterranean Diet (MD) and Plant-Based Diets (PBD) associated with greater microbial diversity and beneficial bacteria. In contrast, the Western Diet (WD) has been associated with reduced diversity and increased risk of disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe GUTDIET-PT study is a multicentre, cross-sectional observational study aiming to recruit 2,500 healthyparticipants aged 40 to 74. It is part of the GUTBIOME-PT (NCT06741293), a larger project designed to improve colorectal cancer (CRC) screening in Portugal. Socio-demographic and anthropometric data (weight and height) will be collected via self-administered questionnaires. Dietary intake will be assessed using two non-consecutive 24-hour dietary recalls, and principal component analysis (PCA) will be used to identify and characterise dietary patterns. Additionally, adherence to the MD and PBD will be evaluated using validated tools such as the Mediterranean Diet Adherence Score and the Plant-Based Dietary Index, while the inflammatory potential of the participants’ diet will be assessed through the Dietary Inflammatory Index. Gut microbial composition and functional capacity will be determined by shotgun metagenomic sequencing of faecal samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eBy combining advanced microbiota analysis with detailed dietary assessments through structured nutritional interviews, we aim to provide novel insights into the relationship between dietary patterns and gut microbiota composition and function. Additionally, focusing the study on healthy individuals will allow us to characterise the microbiota in the absence of disease, offering a clearer understanding of the features of a “healthy” microbiome. This study will establish a foundational framework for identifying dietary patterns that promote microbial diversity and functionality, contributing to tailored nutritional recommendations and public health strategies aimed at improving gut health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration:\u003c/strong\u003eNCT06741293\u003c/p\u003e","manuscriptTitle":"The role of diet and dietary patterns in the composition of gut microbiota (GUTDIET-PT): A multicentre cross-sectional observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 15:09:28","doi":"10.21203/rs.3.rs-9169337/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b0654ab-a545-4f13-ba68-c491352e9f26","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-11T09:19:17+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T09:32:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 15:09:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169337","identity":"rs-9169337","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}