Personal traits, lifestyle decisions, and geography shape our dietary intake and consequently our bacterial and fungal gut microbiome

Personal traits, lifestyle decisions, and geography shape our dietary intake and consequently our bacterial and fungal gut microbiome | 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 Research Article Personal traits, lifestyle decisions, and geography shape our dietary intake and consequently our bacterial and fungal gut microbiome Zaida Soler, Gerard Serrano-Gómez, Marc Pons-Tarin, Sara Vega-Abellaneda, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4990604/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 Global Burden of Disease 2017 (GBD-2017) study identified high sodium intake, low whole grain intake, and low fruit consumption as key dietary risk factors for non-communicable diseases (NCDs). We hypothesize that individual characteristics and lifestyle factors influence these dietary risks, thereby modulating the composition of the gut bacterial and fungal communities. Results From 2020 to 2024, we enrolled 1001 participants from four Spanish regions. Participants completed a short Food Frequency Questionnaire (sFFQ) at baseline, month six, and month 12 (n = 2475). Age, gender, geography, and seasonal factors significantly shaped dietary patterns, with older age and healthier diets, especially those rich in fruits and vegetables, linked to increased gut microbiome diversity. Participants generally consumed less legumes, whole grains, and nuts but exceeded recommended red meat and sugar intake levels, with men showing poorer dietary habits and faster gut transit times correlating with distinct microbiome profiles and lower diversity. Using machine learning techniques, dietary intake can be predicted by the gut microbiome composition. Participants can learn about the study, their diet and their microbiome here (https://manichanh.vhir.org/POP/;username:reviewers;password:reviewers) Conclusion Adherence to national dietary guidelines, particularly the Mediterranean diet, enhances gut microbial diversity. Personal, lifestyle, and geographic factors significantly influence dietary quality, highlighting the need for targeted interventions. The study suggests that improving dietary patterns positively impacts the gut microbiome and overall health in Spain. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Habitual diet and geography have been suggested as among the strongest explanatory factors for human gut microbiota variation. A specific habitual diet may contribute to health and chronic conditions, such as obesity, metabolic syndrome, and inflammatory bowel disorders (IBD). These conditions and associated mortality/morbidity have risen dramatically over the past decades, with the gut microbiome implicated as one of the potentially causal human-environment interactions ( 1 ). In 2019, the Global Burden of Disease (GBD) Study assessed the impact of dietary habits on non-communicable diseases (NCDs) globally ( 2 ). Using a comparative risk assessment approach, the researchers analyzed the consumption of major foods and nutrients across 195 countries. The findings revealed that in 2017, approximately 11 million deaths and 255 million disability-adjusted life-years (DALYs) were attributable to suboptimal dietary habits. High sodium intake, low intake of whole grains, and low intake of fruits were identified as the leading dietary risk factors for both deaths and DALYs worldwide. Overall, the research emphasizes the urgent need for improving dietary patterns globally to mitigate the burden of NCDs. Previous studies have identified significant variations in the gut microbial community among individuals, which has hindered the discovery of microbial species as reliable disease biomarkers. Various factors, including age, medication use, bowel habits, health status, anthropometric characteristics, habitual diet, and lifestyle, have been identified as potential contributors to this high microbiome variability ( 3 ). Consequently, these variations necessitate a large cohort size to effectively discover and validate biomarkers. Over the last decade, population studies have emerged to understand the role of habitual diets on health and disease. These large-scale studies, involving hundreds to thousands of participants, included both non-European countries such as the USA ( 4 , 5 ), Canada ( 6 ), and China ( 7 ), and European countries such as Belgium ( 3 ), and the UK ( 8 ). These studies exemplify large-scale projects that facilitate human microbiome hypothesis generation and testing on an unprecedented scale. They have uncovered associations between microbiome signatures and specific genetic variants, geographic variation, medication, and dietary habits. Although the Spanish diet has been investigated in large-scale studies as part of the Mediterranean diet in relation to cardiovascular disease risk ( 9 , 10 ), no studies have yet comprehensively explored the association between the Spanish diet and both the gut bacterial and fungal microbiome using shotgun metagenomics at the population level. In this study, we aimed to understand the relationship between diet and the microbiome to provide insights into how national nutritional recommendations can influence the microbial ecosystem and, consequently, human health. We collected personal and dietary data from a large population cohort, computed eating quality indexes from the dietary data, and correlated all collected data with microbiome data. Our findings demonstrated that lifestyle and demographic factors significantly influence specific dietary intake, which in turn affects bacterial and fungal microbiome composition and diversity. Methods Participant’s recruitment. We conducted a prospective longitudinal study in accordance with the Declaration of Helsinki, approved by the local Ethics Committee of Vall d’Hebron University Hospital, Barcelona (PR(AG)84/2020). Participants were enrolled in the study between December 2020 and March 2024 through announcements on social platforms such as Facebook, LinkedIn and Instagram, as well as the Hospital Vall d’Hebron website. We recruited 1001 participants from different regions of Spain, aged 18 to 75, who had not taken antibiotics for at least three months and had no diagnosed chronic intestinal disorders, including inflammatory bowel diseases, type 2 diabetes, and autoimmune diseases, before entering the study. All participants signed a consent form. To calculate the sampling fraction for each region area, we first downloaded the relevant data from the Instituto Nacional de Estadística (INE) ( https://www.ine.es/jaxiT3/Tabla.htm?t=2853&L=0 ) regarding the number of males and females between 18 and 75 years old in each autonomous community. We then calculated the population size for the selected region areas (Interior, North of Spain, Mediterranean and Islands) by summing up the individuals from the corresponding autonomous communities. Using these values, we estimated the theoretical percentage for a sample size of 1000 individuals as follows: Theoretical percentage = (1000 x Population in each region area)/Total population in Spain. To evaluate how accurately we achieved our recruitment goal, we divided the actual number of individuals recruited in each region area by the theoretical values. This resulted in a ratio ranging from 0 to 1, where a ratio closer to 1 indicates more accurate recruitment. Metadata and sample collection. Participants filled out an in-house validated short food frequency questionnaire ( 11 ), which provided demographic, lifestyle, clinical, and dietary data, and shipped their stool samples to the microbiome laboratory at baseline, month six, and month 12. The questionnaire was administered online ( https://manichanh.vhir.org/sFFQ/login.php , user:reviewers; password:reviewers). It included 58 food items divided into 13 sections (Supplementary Table S8): vegetables, legumes, and potatoes; fruits and dried fruits; cereals and derivatives; milk and derivatives; eggs, fish, and meat; selfish; oils and fats; bakery and pastry; sauces; non-alcoholic drinks; alcoholic drinks; processed food and others. Frequency of consumption was categorized into six possible options: “Never”, “1 or 3 times per month”, “1 or 2 times per week”, “3 or more times per week”, “once per day”, and “2 or more times per day”. Serving size consisted of a “standard portion” estimated using the ENALIA2 Survey ( 12 ) as well as our own expertise, “half of the standard” and “double of the standard”. To facilitate the estimation of the amount of food consumed by the participants, we added colored photographs. Additional information such as age, sex, weight, height, birth type, smoking, blood type, specific diet, consumption of ready-to-eat food or sweeteners, liquids and supplements, or medication was also recorded. The participants also self-collected their stool samples. The samples were preserved in 95% ethanol at room temperature and then shipped by the participants to the microbiome lab, where they were stored at -80ºC until DNA extraction. Dietary data processing. The first step in converting the dietary information collected from the sFFQ was to transform monthly consumption into daily consumption: for instance, a consumption response of 1–2 times per week was interpreted as an average consumption of 1·5 times per week, which, when divided by the seven days of the week, yielded an average daily consumption of 0.21. Subsequently, this consumption value was multiplied by the weight associated with the selected serving size. For instance, for the legume item with a serving size of 150 g and the aforementioned consumption frequency, the final value of grams per day would be 0.21 x 150 g = 31·5 g/day. The values for the other consumption frequencies were as follows: 1–3 times per month = 0.066; +3 times per week = 0.64; once per day = 1; +2 times per day = 3. Using this gram-per-day information, the energy and nutritional value of each item in the sFFQ were then calculated utilizing a custom-developed food composition database ( 11 ). We calculated the magnitude of the influence of specific participant’s characteristics on dietary intake using permutational analysis of variance (PERMANOVA), as implemented in the adonis2 function of the vegan R package ( https://cran.r-project.org/web/packages/vegan/index.html ) with the Bray-Curtis method. The correlation between eating quality indexes and continuous population characteristics was calculated using the Spearman correlation test. For categorical data, the Mann-Whitney U test was used. Dietary indexes. We utilized various eating quality indexes to assess the nutritional quality of diets. These indexes encompass the Healthy Eating Index-2015 (HEI-2015), the IASE (derived from its Spanish acronym 'Índice de Alimentación Saludable para la Población Española´), the plant-based dietary indexes PDI, uPDI (u = unhealthy), hPDI (h = healthy), the Healthy Food Diversity Index (HFD-index) and the Alternative Mediterranean Diet (aMED) score. The HEI-2015, developed by the United States Department of Agriculture (USDA), is a scoring system designed to provide recommended nutritional guidelines to promote health and prevent chronic diseases ( 13 ). It assesses the intake of different food groups and nutrients, assigning scores to components such as fruits, vegetables, whole grains, dairy, protein foods, fatty acids, refined grains, sodium, added sugars, and saturated fats. Higher scores indicate better adherence to dietary guidelines, with the maximum score for each component representing optimal intake according to the guidelines. The IASE is a modified version of the HEI-2005, specifically tailored to assess the dietary quality of the Spanish population in 2011 ( 14 ). Similar to the HEI-2005, the IASE evaluates dietary patterns and adherence to dietary guidelines, but with considerations for the specific food choices and dietary habits commonly found in Spain. The IASE takes into account various components of the diet, including the consumption of fruits and vegetables, cereals and grains, proteins, dairy products, fats and oils, sweets, pastries, and alcoholic beverages. It assesses the quality of these food groups based on recommended intake levels and patterns that are more relevant to the Spanish diet and nutritional guidelines. Introduced by Satija et al. in 2017 ( 15 ), the PDI, uPDI, and hPDI evaluate the quality of a person's diet based on various aspects of dietary intake in the US. The PDI assesses the proportion of plant-based foods consumed relative to animal-based foods. A higher PDI score indicates a diet richer in plant-based foods like fruits, vegetables, whole grains, nuts, and seeds, with lower consumption of animal-based foods such as meat and dairy. The uPDI focuses on less healthy plant-based items like refined grains, potatoes, and sweets, with a higher score suggesting an increased intake of these less nutritious plant-based foods. In contrast, the hPDI emphasizes the consumption of healthier plant-based foods within a plant-based diet, such as fruits, vegetables, whole grains, nuts, and legumes, with a higher hPDI score reflecting a diet rich in these nutrient-dense plant-based food groups. The HFD, developed by Dresher et al. in 2007 ( 16 ), measures food intake diversity by evaluating the intake of various food groups including fruits, vegetables, whole grains, lean proteins, and healthy fats. A higher HFD-index score generally indicates a more diverse and nutritious diet. The aMED score corresponds to a scoring system developed by Fung et al. ( 17 ), which is based on the original Mediterranean diet scale proposed by Trichopoulou et al. ( 18 ). The aMED score ranges from 0 (indicating minimal adherence) to 9 (representing perfect adherence) points and evaluates adherence to nine food groups: 1) All kinds of vegetables excluding potatoes; 2) Legumes including tofu, beans, and peas; 3) Fruits and fruit juices; 4) Nuts including peanut butter; 5) Whole grains; 6) Red and processed meat; 7) Fish and shellfish; 8) Ratio of monounsaturated to saturated fat; 9) Alcoholic drinks. For each category, including the fatty acid ratio, the median intake was calculated in grams per day. Healthy food groups (vegetables, legumes, fruits, nuts, whole grains, fish, and the fatty acid ratio) were scored with 1 if the participant's intake was above the median and 0 if it was below. Conversely, for red and processed meats, 1 point was assigned if participants reported lower intake compared to the median, while 0 points were given for higher intake. Alcoholic drinks were scored differently. For men, consumption between 10–50 grams per day or 5–25 grams per day for females received 1 point, while intake outside these ranges received a score of 0. Comparison of dietary intake with recommendations from the GBD-2017 consortium. To compare major food and nutrient consumption within the context of the Global Burden of Disease (GBD) study, we grouped our semi-quantitative sFFQ items into 12 out of the 15 proposed dietary risk factors defined by the GBD, aiming to align with their dietary profiles. We calculated the median intake in grams per day for fruits, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, sugar-sweetened beverages, fiber, and calcium, and compared these values with the optimal and optimal range of intake defined in the GBD study. For polyunsaturated fatty acids (PUFAs), we calculated their consumption percentage relative to the total energy intake and compared it with the GBD recommended values. Sodium was omitted from our analysis as our data only reflected sodium present in food and did not account for sodium added during cooking. Additionally, seafood omega-3 and trans fatty acids were not evaluated due to the absence of these variables in our sFFQ. Supplementary Table S3 listed the clustering of items into the dietary risk factors as suggested by the GBD consortium. Microbiome analysis. DNA extraction. An in-house protocol was used to perform extraction of the genomic DNA from stool samples. This protocol contains a beat beating step to break-down microbial cell walls. A frozen aliquot (200 mg) was extracted as described in our previous paper ( 19 ). Microbiome sequencing. The DNA shotgun library was prepared and sequenced using the Illumina NovaSeq6000 platform. The sequencing process provided an average of 5 Gb of sequence data per sample. The KneadData v0.7.4 pipeline was used to pre-process and decontaminate the sequence reads ( https://huttenhower.sph.harvard.edu/kneaddata ). KneadData performed a quality filtering of the reads using trimmomatic and then mapped them against a human reference genome database using Bowtie 2. Reads with lengths below 50% of the total input length and also those that mapped with the human genome were discarded from further analysis. Taxonomic profiles were provided by the MetaPhlan4’s intermediary output file in the HumanN3 pipeline and functional profiles from the final output ( 20 ). Taxonomic profiles, the outputs of MetaPhlan4, were generated in stratified relative abundance, from phylum to SGB level. For this reason, no normalization was applied, but the stratified relative abundances were extracted according to the taxonomic species level. Alpha and beta diversity analyses were performed using Chao1 and Shannon indexes ( 19 ) and the adonis2 function (Permutational Multivariate Analysis of Variance), respectively. Functional profiles, the output of HumanN3, provided gene families and MetaCyc pathways. MetaCyc pathways were filtered to remove unmapped and unintegrated reads. All features that did not achieve 0.001 abundance and 0.1 prevalence (pathways that did not achieve 0.1% of the total sample abundance in at least 10% of the samples) were also discarded. Then, pathways were sum-normalized to counts per million (CPM) before further analysis. Statistical analyses for microbiome sequence data were performed in R (v4.3). Covariates such as gender, age, body mass index (BMI), region areas, smoking habit, season, and workplace were tested for their impact on microbiota variation using the PERMANOVA test on weighted and unweighted UniFrac distance indexes. We assessed the ability of the microbiome to predict distinct food items, food groups, and nutrients using the random forest classification and regression algorithms ( 8 ). For both the regression and classification processes, a cross-validation approach was used based on 100 bootstrap iterations and an 80/20 random split of training and testing folds. For the classification task, item, food group, and nutrient frequencies were divided into the first and fourth quartile, used as the two classes to predict. Both regression and classification algorithms were trained on species-level genome bin (SGB)-level features as estimated by MetaPhlAn4. Classification was evaluated using the median AUC, while regression used the median Spearman’s correlation between the actual and predicted values. Given the compositional nature of the sequence data, differential abundance (DA) analysis of the microbial community was performed using MaAsLin2 (Multivariate Association with Linear Models) ( 21 ). The analysis tested for differences in population characteristics while including gender, BMI, and age as confounding factors. The resulting p-values were corrected for false discovery rate (FDR). Associations identified by MaAsLin2 were considered significant if the coefficient, measuring the strength and direction of association, was greater than 1 (in most cases) and the q-value was less than 0.05. Spearman tests were used to correlate dietary data with microbiome data. For functional analysis, Spearman's correlation between alpha diversity indexes (Chao1 and Shannon) and pathway abundances were computed and FDR corrected. Correlations with − 0.4 = 0.4 and FDR < 0.05 were considered significant and kept for further analyses. Association analysis was performed between these pathways and food items, food groups, and nutrients using the Spearman correlation test. To assess changes in the potential pathways of the microbial community depending on personal information, we used linear models as implemented in MaAsLin2, adjusting for bowel movement (transit time), gender, BMI, age, smoking habit, region area, and season years as fixed-effects, using MetaCyc pathways information. To increase the interpretability of these results, pathways were grouped into their MetaCyc parent instances up to 7 levels, in which each level represents broader biological function, with level 1 being the broadest and 7 the most specific. Pathways with more than one parent instance were duplicated and assigned to different parents for plotting and interpretation purposes. Fungal enrichment procedure, sequencing, and statistical analysis To improve the detection of fungal species, a subset of 100 samples was selected to apply the fungal enrichment protocol described by Xie et al. ( 22 ). The fungal partition was processed for genomic DNA extraction and shotgun metagenomic sequencing, as previously described, while the bacterial partition was discarded. Fungal profiling was performed on both the 99 enriched samples (1 sample failed during the enrichment procedure) and the 500 non-enriched samples. We used KneadData v0.7.4 for read quality control and decontaminating human sequences. Then, FunOMIC2 database and pipeline were used to obtain the taxonomic and functional fungal profiles. Raw counts were normalized with the CPM method implemented in the “edgeR” R package. Fungal alpha diversity was assessed by computing Chao1 index on raw fungal species counts, and Shannon index on CPM-normalized counts. Beta diversity was assessed by computing Bray-Curtis distances. To compare samples with and without fungal enrichment, total annotated fungal reads and alpha diversity were tested for significant differences with a paired Wilcoxon test. Beta diversity was compared with the Adonis PERMANOVA test. Further analyses on fungal composition were performed on the 99 enriched samples. Alpha diversity was compared between gender, smoking habit, region, and season groups with the Mann-Whitney U test and was correlated with the Spearman correlation test with age and BMI, diet indexes, food items, food groups, and nutrients. Spearman correlation was also used to find associations between fungal species and dietary information. The p-values were adjusted using the Benjamini-Hochberg method. We considered q-values (adjusted p-values) significant when < 0.05. Website construction We built a website dedicated to this study ( https://manichanh.vhir.org/POP/ , username:reviewers, password:reviewers ), where participants can access an overview of the results of this research, as well as their personal information on nutrient intake and dietary indices (based on the short food frequency questionnaire), and, if available, their microbiome sequencing results, including bacterial and fungal composition, and measures of alpha diversity. Nutrient intake data are compared to the guidelines established by the Scientific Committee of the Spanish Agency for Food Safety and Nutrition (AESAN), while dietary indices and alpha diversity scores are compared to the population median found in this study. Participant reports are produced dynamically in the form of a Shiny app ( https://shiny.posit.co/ ) , which is run in R language and hosted in our local Shiny server. All personal results are anonymized and password-protected, ensuring each participant may only access their own information. Results Cohort characteristics and collected metadata and samples Between 2020 and 2024, we enrolled 1,001 participants from four regions in Spain, covering all 17 autonomous communities (Fig. 1AB). The cohort consisted of 458 men and 542 women, all over 18 years old. None of the participants had taken antibiotics for at least three months before the study began, and none had any diagnosed chronic intestinal disorders. Further details regarding the cohort's characteristics can be found in Supplementary Table S1 . We employed an in-house ( 11 ) online short Food Frequency Questionnaire (sFFQ) to gather demographics, biometrics, lifestyle, and dietary data. Participants filled out the sFFQ at baseline (n = 1001), month six (n = 822), and month 12 (n = 652), resulting in a total of 2475 completed sFFQs. Additionally, at baseline, stool samples (n = 500) were collected concurrently with the sFFQ for comprehensive analysis. These samples underwent microbiome compositional and functional profiling through shotgun sequencing (Fig. 1CD). Personal traits, lifestyle decisions, and geography influence the quality of dietary intake (n = 1001) The collected 58 food items, from 2475 sFFQs, were categorized into 24 food groups and 32 macro- and micronutrient contents (refer to the Methods section). We then investigated the relationship between covariates such as lifestyle, biometrics, and demographic factors on dietary intake using the adonis method, also known as Permutational Multivariate Analysis of Variance (PERMANOVA). These self-reported covariates included age, geography, workplace (hospital or non-hospital), gender, BMI, season, dietary types, smoking status, sweetener consumption, menstruation or menopause status (if applicable), and bowel habits. All covariates, except for workplace, were found to be significantly associated with the composition of food items and food groups (Fig. 2 A). Additionally, seven of these covariates (region areas, gender, season, dietary types, smoking status, sweetener consumption, and bowel habits) were associated with variations in macro- and micronutrient intake (PERMANOVA, p < 0.05, Fig. 2 A). Taking advantage of the longitudinal setting of the study, we analyzed the intra- and inter-variability of food intake using the Bray-Curtis similarity index for food items, food groups, and nutrient data. As expected, we found that intra-individual variability (with sFFQs analyzed six months apart) was lower than inter-individual variability across all three dietary classifications (p < 2·2 x 10^-16, Supplementary Figure S1 ). This suggests a relatively stable intra-individual dietary pattern across different seasons at all dietary levels. Next, we examined how differences in population characteristics may explain variances in several eating quality indexes (EQIs), which were developed based on well-established national guidelines to evaluate the nutritional quality of individuals' diets and their adherence to recommended dietary patterns (refer to the Methods section for comprehensive explanations). To achieve this, we initially utilized the collected food items, food groups, and nutrients to calculate various EQIs (HEI-2015, IASE, HFD, hPDI, uPDI, and the aMED). Subsequently, we employed linear regression models, implemented in MaAsLin2, to assess the impact of different population characteristics on these EQIs while controlling for potential covariates mentioned above. Increasing age was found positively associated with a healthier diet as indicated by two EQIs (q(IASE) = 0.03; q(hPDI) = 7·1x10^-07) (Fig. 2 B) and with several food groups such as alcoholic beverages, whole bread, nuts and seeds, fruits, and fruit products (Supplementary Table S2 ). Men exhibited lower values of IASE, hDPI, aMED, and HFD, and higher values of uDPI compared to women, indicating poorer dietary habits among men (Fig. 2 B). Men's dietary patterns were more associated with the consumption of ready-to-eat meals (q = 0.038) and alcoholic beverages (q = 0.00014), whereas women showed higher consumption of whole bread (q = 0.013), vegetables (q = 5x10^-09), nonalcoholic drinks (q = 0.0019), fruits and fruit products (q = 0.002), fish and shellfish (q = 0.00024), but also higher intake of fats and oils (q = 2·7x10^-07) (Supplementary Table S2 ). Geographically, we divided Spain into four regional areas: the Mediterranean, the Interior, the North, and the Islands (Fig. 1 B). This classification considers traditional Mediterranean diet patterns, geographical distribution, and socio-economic factors, all of which can influence dietary habits and patterns ( 23 ). Compared to the Mediterranean region, the Interior exhibited a healthier dietary pattern based on the three eating quality indices (aMED, uPDI, HEI_2015) (Fig. 2 B), characterized by a higher consumption of legumes (q = 0.013, Supplementary Table S2 ). Interesting positive associations were identified between population behaviors and specific food groups (Supplementary Table S2 ). For instance, the use of sweeteners was correlated with the consumption of sugar (q = 0.044), ready-to-eat meals (q = 0.000018), sauces and condiments (q = 0.00042), and sausages and other meat products (q = 0.018). Additionally, smoking (q = 0.0011) or past smoking (q = 0.0031) habits were associated with alcohol consumption. Partial alignment with recommendations from the GBD-2017 To evaluate whether the dietary intake of our population aligned with the recommendations of the Global Burden of Disease (GBD) Study 2017 (GBD, 2019, PMID: 30954305), we categorized our 58 sFFQ items (n = 1001; 2475 sFFQs) into 12 of the 15 GBD dietary risk factors (refer to the methods section, Supplementary Table S3). Our cohort's intake of fruits, vegetables, and fiber met the recommended ranges set by the GBD study (Supplementary Table S4). However, we observed suboptimal intake levels for legumes, polyunsaturated fatty acids (PUFA), whole grains, nuts, milk, and calcium compared to GBD recommendations. Additionally, the intake of red meat, processed meat, and sugar-sweetened beverages exceeded the levels recommended by the GBD guidelines. Demographic, anthropometric, and dietary data correlate with bacterial microbiome data Next, to assess the effect size of population characteristics on the microbiome, we employed the adonis2 method based on Bray-Curtis distances of microbiome sequence data. Specifically, gender, age, and BMI demonstrated significant impacts on microbiome composition at the global level (Supplementary Figure S2 ). These covariates were subsequently used as possible confounders in downstream association analysis. Although there is no definitive evidence in the literature establishing a direct link between high gut microbial diversity and healthy status, several disorders, including inflammatory bowel diseases ( 24 , 25 ), obesity ( 26 ), and diabetes ( 27 ), have consistently been associated with low microbial diversity. These associations suggest that a diverse gut microbiome plays a role in maintaining health. Using the Spearman test, we assessed the correlation between population characteristics, dietary data, and microbiome diversity. The results showed that diversity (based on the Shannon index) was positively associated with vegetable intake (rho = 0.12, q = 0.0088), fruits (rho = 0.15, q = 0.0019), fiber intake (rho = 0.1, q = 0.026), and nuts and seeds (rho = 0.12, q = 0.018), while white bread was negatively linked to microbial diversity (rho=-0.15, q = 0.009) and white grains (rho=-0.16, q = 0.00304) (Fig. 3 ). This is further supported by the positive correlations between the Shannon index and dietary indexes such as the HEI-2015 (rho = 0.13, q = 0.0031), the hPDI (rho = 0.13, q = 0.0026), the aMED index (rho = 0.13, q = 0.0047), which emphasize fruit and vegetable consumption, while the uPDI was negatively correlated with diversity (rho=-0.14, p = 0.0016) (Fig. 3 ). These results suggest that adherence to national dietary guidelines and recommendations was associated with increased microbial diversity. Additionally, diversity (Shannon index, rho = 0.12, p = 0.006) and richness (Chao1 index, rho = 0.15, p = 0.007) positively correlated with age, reinforcing the connection between older age and healthier eating habits (Fig. 3 ). On the contrary, BMI (rho=-0.11, p = 0.011), and uPDI (rho=-0.11, p = 0.01) index were found negatively correlated with both richness and diversity. Given that age was also associated with BMI (rho = 0.31, p < 0.05), these findings suggest that higher diversity is linked to older age and lower BMI. We also observed a seasonal effect on dietary intake and microbiome diversity, with higher bacterial diversity in summer compared to winter (p = 0.048), which could be due to a higher consumption of fruits and vegetables in summer (Fig. 4 ). Association analysis between pathways and dietary data revealed significant correlations between the L-arginine biosynthesis II and sucrose biosynthesis II pathways and the consumption of fruits, nuts, and seeds. At the nutrient level, significant associations were also found with fiber intake (Supplementary Table S5). These findings suggest that diet can influence not only the composition of the gut microbiome but also its functional capabilities. The extent to which transit time (bowel movement) influences the microbiome is still not well understood. To address the question related to the impact of transit time on the microbiome community, we examined the association between defecation frequencies obtained from the sFFQs (categorized as 1·5 times/week, > 3 times/week, 1 time/day, 2 times/day, and > 2 times/day) on microbiome diversity and composition using the Wilcoxon test and general linear models (MaAsLin2), respectively. Our results indicated that longer transit times were associated with higher diversity (p < 0.05, Fig. 4 ). Additionally, we observed that microbiome diversity appeared to stabilize at a defecation frequency of more than 3 times per week, as indicated by non-significant differences in the Chao1 and Shannon indexes between defecating more than 3 times per week and 1·.5 times per week. At the compositional level, using one defecation per day as a reference, 20 bacterial species (including Akkermansia muciniphila ) were positively associated, while three species (including Lacrimispora amygdalina and Blautia wexlerae ) were negatively associated with longer transit times (more than three times and only 1·5 times per week). Conversely, one species ( Ruthenibacterium lactatiformans ) was negatively associated with short transit times (> 2 times per day). (Supplementary Table S6). At the functional level, longer transit times were associated with more pathways than lower transit times. These pathways include fermentation, glycan, amine degradation, amino acids degradation and biosynthesis, and lipid biosynthesis, while shorter transit times were more linked to carbohydrate degradation (Fig. 5 ). Other correlations were found between microbiome and demographic and biometric data including age, BMI, gender, season, and smoking (Fig. 4 , Supplementary Table S7). Interestingly, BMI, which correlated with three bacterial species, also correlated with 39 pathways (26 positive and 13 negative correlations). Correlation with fungal microbiome Using our standard extraction protocol optimized for bacterial DNA recovery, we detected fungal reads in only 340 out of 500 fecal samples. Additionally, the number of fungal reads recovered was extremely low, with a median of just 2 reads per sample, which is insufficient for proper mycobiome characterization. Therefore, we performed an enrichment procedure using an in-house method, as described in Xie et al. ( 22 ), on a randomly selected subset of 100 samples, matched for gender. Two samples did not pass the quality control test during the library construction step. Fungal reads were then detected in all the 98 remaining samples with a median of 25 reads per sample, compared to 73 without the enrichment protocol. A total of 141 different species were detected in the enriched samples, compared to 45 species in the non-enriched samples. We also observed a significant increase in alpha diversity based on Chao1 and Shannon indexes with the enrichment protocol (Supplementary Figure S3). The top five most prevalent species in the enriched samples were Saccharomyces cerevisiae (80 samples), Malassezia restricta (33 samples), Debaryomyces hansenii (25 samples), Penicillium roqueforti (21 samples) and Meira nashicola (21 samples). Similar to the bacterial microbiome, we evaluated the association between population characteristics, dietary intake, and the fungal microbiome diversity, and composition. The few significant correlations found included age, seasons, and the uPDI index. Fungal diversity increased with age (rho(Shannon) = 0.2, p = 0.044) and was higher in spring/summer compared to autumn/winter (p(Chao1) = 0.022; p(Shannon) = 0.0056). This diversity decreased with the uPDI index (rho(Shannon) =-0.26, p = 0.011) (Fig. 4 ). No strong association was found between fungi and dietary data. Prediction of dietary intake by the gut microbiome The “GBD 2017 Diet Collaborators” reported in 2019 that high intake of sodium, and low intake of whole grains and fruits were the leading dietary risk factors for deaths and years of life adjusted for disability ( 2 ). In our study, sodium was not properly evaluated in the questionnaire, as we did not add any specific question related to the added sodium during the cooking process, therefore we cannot assess the impact of salt on the microbiome. However, using the machine learning approach to microbiome features and the reported dietary data, we showed that several food items were strongly associated with microbiome composition. These food items included coffee with and without caffeine (rho = 0.41, AUC = 0.82), nuts and seeds (rho = 0.25, AUC = 0.76,), vegetables (rho = 0.19, AUC = 0.67), fruits (rho = 0.19, AUC = 0.66), fermented dairy (rho = 0.18, AUC = 0.74), and dark chocolate (rho = 0.18, AUC = 0.66). The analysis using food groups validated the findings with nuts and seeds (rho = 0.24, AUC = 0.75), fruits (rho = 0.20, AUC = 0.68), milk and dairy (rho = 0.20, AUC = 0.65), vegetables (rho = 0.19, AUC = 0.67), yogurt (rho = 0.17, AUC = 0.73), and chocolates (rho = 0.16, AUC = 0.66) (Fig. 6 ). Using Spearman correlation test, coffee was found associated with five bacterial species: Clostridium phoceensis (rho = 0.4, q = 0), Massilioclostridium coli (rho = 0.34, q = 4·7x10^-12), Clostridium bacterium_12CBH8 (rho = 0.26, q = 20.9x10^-06), two unannotated bacterial species: GGB9494_SGB14891, GGB9557_SGB14966 (rho = 0.25, q = 2·9x10^-06). Nuts and seeds were found linked to three annotated bacterial species: Lachnospiraceae bacterium (rho = 0.23, q = 0.0002), Flavonifractor plautii (rho=-0.19, q = 0.014), and Roseburia hominis (rho = 0.18, q = 0.18) and one unannotated species (GGB3478_SGB4643, rho = 0.22, q = 0.0006). Vegetables were positively associated with three unannotated bacterial species (Clostridium_sp AF20_17LB (rho = 0.2), Bacilli unclassified SGB6422 (rho = 0.18, Lachnospiraceae unclassified SGB5063, rho = 0.18)) and negatively associated with Flavonifractor plautii (rho = 0.19, q = 0.014). Fruits were positively correlated with two unknown species (GGB9758_SGB15368, GGB4676_SGB6465), Lachnospira eligens (rho = 0.19, q = 0.011) and an unclassified Bacilli (SGB6473, rho = 0.18, q = 0.013). Fermented dairies including yogurt and kefir were associated with Streptococcus thermophilus (rho = 0.32, q = 5·710^-10) and to a lesser extent with Bifidobacterium animalis (rho = 0.17, q = 0.11). Chocolate (> 50% cacao) was positively associated with Clostridium_sp_AF32_12BH (rho = 0.18, q = 0.018) and two unclassified bacterial species (GGB52930_SGB73859, rho = 0.20, q = 0.006; GGB3478_SGB4643, rho = 0.19, q = 0.010). No strong prediction could be recovered from fungal sequence data. Website for the Citizen science project This project was designed to engage the public in data collection and raise awareness about scientific research. Participants contributed by providing their dietary data and stool samples. Through the website created for this project ( https://manichanh.vhir.org/POP/ , username:reviewers, password:reviewers), participants were able to collect their dietary information using the sFFQ and ship their stool samples to the microbiome lab. Participants learned about the overall study findings and accessed their personal dietary and microbiome profiles. The website also offers resources to help participants understand the significance of their contributions and the impact of the research. Discussion This study uncovered new insights into the complex interplay between EQIs, personal traits, lifestyle choices, geography, and diet, and their impact on the gut microbial community, revealing how national dietary recommendations can influence this community. EQIs have been developed to serve as comprehensive tools for evaluating diet quality and guiding dietary recommendations. Researchers use EQIs to facilitate research on how diet affects the risk of chronic diseases, such as obesity, diabetes, cardiovascular diseases, and certain cancers ( 28 ). In the present study, the assessment of the impact of the population characteristics on the nutritional quality revealed crucial insights into how age, gender, geographical location, and lifestyle shape eating habits. Our findings, reporting healthier dietary habits as we age, are validating previous works that showed that older adults have a more “prudent” dietary pattern characterized by higher intakes of vegetables, fruits, whole grains, nuts and seeds ( 29 , 30 ). In our study, we excluded individuals older than 75 years to avoid potential confounding factors, such as age-related undiagnosed diseases like frailty or early stage neurodisorders. Through the analysis of self-reported defecation frequency, we showed transit time is a significant factor influencing gut microbiome diversity and composition. Longer transit times, associated with higher microbial diversity, could potentially support gut health by enabling a more resilient and varied microbial ecosystem. Conversely, very rapid transit times (> 2 times per day) might limit microbial diversity and favor certain species over others, potentially impacting overall gut health. The stabilization of diversity at moderate defecation frequencies indicates a potential balance point that might be optimal for maintaining a healthy microbiome. Our results validate the work from Asnicar et al. ( 31 ) assessing the relationship between gut transit time and the human gut microbiome, using the blue dye method. They also reported that longer gut transit time was associated with higher bacterial diversity and specific microbial species. Using a machine learning approach, the study identified key food items and food groups strongly associated with microbiome composition. Coffee, nuts and seeds, vegetables, fruits, fermented dairy, and dark chocolate emerge as significant predictors of microbial composition. As a Mediterranean country, Spain's traditional diet is rich in fruits, legumes, whole grain cereals, vegetables, nuts, and healthy unsaturated fats primarily from olive oil. It also includes frequent fish intake, moderate consumption of dairy products and fermented beverages, and a low intake of meat and meat-derived products ( 32 ). Despite its benefits, adherence to the Mediterranean diet (MD) in Spain has decreased over time, shifting towards a more Western dietary pattern ( 33 – 35 ). The influence of regional dietary habits, particularly within Mediterranean countries, is well-known. Our study’s division of Spain into the Mediterranean, Interior, North, and Islands, and its identification of healthier dietary patterns in the Interior region, aligns partially with prior research showing geographical variability in adherence to the Mediterranean diet and other dietary patterns ( 36 ). Moreover, our study showed that individuals from the Interior region were characterized by higher consumption of legumes, which offer a range of health benefits due to their rich nutrient content and bioactive compounds including protein, fiber, vitamins, and minerals. Among the dietary variables proposed by the Global Burden of Disease study, our Spanish cohort complied with only 3 out of the 12 food groups analyzed: vegetables (321·48 g/day), fruits (225·6 g/day), and fiber (27·32 g/day). These three groups were related to higher alpha diversity and correlated with bacterial species with potential health implications. For instance, vegetables were negatively correlated with Flavonifractor plautii , a flavonoid-degrading bacterium associated with less healthy diets, lower scores in EQIs, and related to disease outcomes such as colorectal cancer, inflammatory bowel disease (IBD), depression, and bipolar disorder. The association analysis of food group consumption reveals gender-specific dietary behaviors. It is recognized that women generally exhibit healthier dietary patterns than men, consuming more fruits, vegetables, and whole grains, while men consume more meat and alcohol ( 37 , 38 ). These findings are validated by our study, which shows that men have a higher consumption of ready-to-eat meals and alcoholic beverages. Low bacterial diversity has been linked to various disorders, suggesting a connection between health status and high microbial diversity ( 24 , 25 ). The present study demonstrates that adherence to national dietary guidelines—particularly increased consumption of fruits, vegetables, fiber, nuts, and seeds—positively correlated with microbial alpha diversity. Conversely, adherence to an unhealthy diet with a high intake of white bread negatively affects microbial richness and diversity. These findings align with previous reports indicating that a high-fiber diet enhances alpha diversity, while a low-fiber diet, such as one high in white bread, reduces it ( 39 ). A key component of this project was the development of a website, which allowed participants to efficiently collect and submit their dietary information using a structured Food Frequency Questionnaire (sFFQ). Beyond data collection, the website provided participants with private access to both the overall study findings and their personalized dietary and microbiome profiles, enhancing their understanding of their contributions. Additionally, we ensured that the website offered comprehensive resources to help participants appreciate the significance of their involvement and the broader impact of the research. This integrated approach not only facilitated data collection but also strengthened the connection between the participants and the scientific community. Analyzing only 500 samples allowed us to uncover similar results to previous studies with larger sample sizes, such as the association between vegetables, fruits, transit time, and diversity, and the role of coffee as a main factor influencing microbiome composition ( 8 ). Despite our efforts to achieve a similar sampling fraction for each of the Spanish regions, our results may be biased due to an over-recruitment of participants from the Mediterranean region. However, to limit the effect of regional differences in the results, region areas were considered as a covariate in the statistical models. Conclusion This study highlights the significant influence of personal traits, lifestyle choices, geography, and dietary habits on the gut microbiome, and underscores the importance of promoting national dietary guidelines to enhance gut microbial diversity and improve health outcomes, emphasizing the need for continued adherence to the Mediterranean diet amidst shifting dietary patterns in Spain. Future research could explore longitudinal studies to further elucidate causal relationships between dietary patterns, microbiome composition, and health outcomes, ultimately paving the way for precision medicine approaches in nutrition and healthcare. Declarations Ethics approval and consent to participate The study was approved by the local Ethics Committee of the Vall d’Hebron University Hospital, Barcelona (Project identification code: PR(AG)84/2020). All participants signed a consent form. Consent for publication All participants consented independently when donating samples. All data obtained and generated during the study were kept confidential. Funding This work was supported by the Instituto de Salud Carlos III/FEDER (PI20/00130; FI21/00262). Marc Pons and Sara Vega-Abellaneda were supported by the AGAUR (2021 SGR 00459). Francisca Yáñez was supported by a fellowship from ANID, BECAS Chile, No. 72190278. Availability of data and materials Data collected for the study include individual participant data and microbiome sequence data. Participants were codified. Upon publication shotgun metagenomic sequencing raw data (short-read archives, SRA) will be made available via NCBI Project Number PRJNA1146994. Any additional information needed to reanalyze the data reported in this work is available upon request from the corresponding author of the manuscript. Competing interests No competing interests. Author’s contribution Z.S. contributed to literature searches, data collection, data analysis, data interpretation, writing, review, and editing. M. P.-T., I. 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Nutr Hosp 2013;28(3):951–957. Varela-Moreiras G, Avila JM, Cuadrado C, del Pozo S, Ruiz E, Moreiras O. Evaluation of food consumption and dietary patterns in Spain by the Food Consumption Survey: updated information. Eur J Clin Nutr 2010;64 Suppl 3:S37-43. Abellan Aleman J, Zafrilla Rentero MP, Montoro-Garcia S, Mulero J, Perez Garrido A, Leal M et al. Adherence to the "Mediterranean Diet" in Spain and Its Relationship with Cardiovascular Risk (DIMERICA Study). Nutrients 2016;8(11). Fenton S, Ashton LM, Lee DCW, Collins CE. Gender differences in diet quality and the association between diet quality and BMI: an analysis in young Australian adults who completed the Healthy Eating Quiz. J Hum Nutr Diet 2024;37(4):943–951. White AM. Gender Differences in the Epidemiology of Alcohol Use and Related Harms in the United States. Alcohol Res 2020;40(2):01. Wang Y, Wymond B, Tandon H, Belobrajdic DP. Swapping White for High-Fibre Bread Increases Faecal Abundance of Short-Chain Fatty Acid-Producing Bacteria and Microbiome Diversity: A Randomized, Controlled, Decentralized Trial. Nutrients 2024;16(7). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures28aug24.docx SupplementaryTables28aug24.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4990604","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352706956,"identity":"14c62366-36b1-464d-b10c-5c380275f071","order_by":0,"name":"Zaida Soler","email":"","orcid":"","institution":"Vall d'Hebron Institut de Recerca","correspondingAuthor":false,"prefix":"","firstName":"Zaida","middleName":"","lastName":"Soler","suffix":""},{"id":352706957,"identity":"471eaeaa-8020-44aa-b33e-51cbc13303e3","order_by":1,"name":"Gerard Serrano-Gómez","email":"","orcid":"","institution":"Vall d'Hebron Institut de 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11:11:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4990604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4990604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66946717,"identity":"3e030f4a-b67b-4729-841e-135c1d525102","added_by":"auto","created_at":"2024-10-18 09:48:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":712765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy design.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e A). 1001 participants reported their dietary intake and personal data through an in-house online short Food Frequency Questionnaire (sFFQ) at baseline, month six, and month 12 (n = 2475). Stool samples (n = 500) were collected at baseline for microbiome analysis. B). Recruitment of participants from different autonomous regions of Spain and sampling fractions. The distribution of participants recruited from the 17 autonomous regions of Spain and the four regional areas is presented. The sampling fraction for each regional area was calculated based on the proportion of the population in each region, as reported by the Spanish government. C). Information from the sFFQs was used to collect personal data and to calculate different Eating Quality indexes (EQIs). Extracted genomic DNA from stools was sequenced through a shotgun metagenomic approach and sequences were processed to analyze microbiome composition and function. D). Association analysis between microbiome and dietary data and diet prediction models. The association was performed using either the Spearman correlation test or the linear models implemented in the MaAsLin2 tool, and the predictions were performed using the random forest classification and regression algorithms.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/a4e3f04c32b2e3e7f8eeee2b.png"},{"id":66948408,"identity":"80776c3e-0000-4948-8567-c6d5ecce3e38","added_by":"auto","created_at":"2024-10-18 09:56:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":610968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRelationship between population characteristics and dietary data.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e A). Effect size of the population characteristics on dietary intake. The magnitude of the influence of specific characteristics on dietary intake was calculated using permutational analysis of variance (PERMANOVA), as implemented in the adonis2 function of the vegan R using the Bray-Curtis method. B). Relationship between Eating Quality Indexes (EQIs) and population characteristics (age, gender, and region areas) was calculated using the MaAsLin2 tool.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/794fc026af7ebe3de94d3470.png"},{"id":66946720,"identity":"310621e6-f4c7-4820-aebb-ad4ff4a2ac4d","added_by":"auto","created_at":"2024-10-18 09:48:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1109850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation between Eating Quality Indexes (EQIs), food groups, and personal data with alpha diversity (Chao1 and Shannon) using the Spearman correlation test (n = 500).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/ddec2c8c1782aa78b174ad91.png"},{"id":66946722,"identity":"318cf7ae-fc88-4920-a08e-1f614801cd2f","added_by":"auto","created_at":"2024-10-18 09:48:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1164019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePopulation characteristics-microbiome alpha diversity association analysis. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThe first five figures represent differences in categorical population characteristics in relation to bacterial alpha diversity (Chao and Shannon indices), analyzed using the Mann-Whitney test (top figures; n = 500). The last three figures represent fungal alpha diversity associations with diet and covariates in enrichment samples (n = 98). On the left side is the Spearman correlation between age and the Shannon diversity index. In the middle is the Spearman correlation between the adjusted uPDI index and the Shannon diversity index. On the right side is a comparison of the Shannon diversity index between different seasons (Mann-Whitney U test).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/beff26ec0ee409ff65cb5164.png"},{"id":66948410,"identity":"49da72a4-2015-4b06-9039-8ef62c0b4eff","added_by":"auto","created_at":"2024-10-18 09:56:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":332434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDifferentially abundant pathways at the metagenomic level. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ePathways are grouped by functionality according to the MetaCyc database and influenced by several conditions. Differentially abundant pathways were compared between low transit time (\u0026gt;3 times per week, 1 or 2 times per week) and the reference (once a day). A). Positive coefficients reflected pathways enriched in low transit time, whereas negative coefficients represented their depletion. B). Differentially abundant pathways also depend on BMI, with positive coefficients indicating a higher abundance of pathways in individuals with higher BMI.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/2890c41f3828707bc0e1fef8.png"},{"id":66948409,"identity":"f2263fa7-7e86-4bd5-af07-8d483a4a07f5","added_by":"auto","created_at":"2024-10-18 09:56:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":284388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrediction using machine learning technique.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003ePrediction of different food items (A), food groups (B) and nutrients (C) using species-level genome bin (SGB)-level features information estimated by MetaPhlAn4. Y-axis and X-axis represent median Spearman's correlation and median receiver operating characteristic area under the curve (ROCAUC) from the random forest regressor and random forest classifier, respectively.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/c9e6c76616d035ad9b1de407.png"},{"id":70591345,"identity":"13c177d2-5893-4a08-ba87-cab26e8a6cc5","added_by":"auto","created_at":"2024-12-04 17:02:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4310567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/34e229b0-5e19-46e6-bbc9-0cb40048bcb4.pdf"},{"id":66946724,"identity":"4844a467-22fa-439b-9653-135f491bbc74","added_by":"auto","created_at":"2024-10-18 09:48:07","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14029906,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures28aug24.docx","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/c698d6d3b7867afbffb0d6ab.docx"},{"id":66946718,"identity":"7cb6f255-6c80-4a8c-905f-c742e6907751","added_by":"auto","created_at":"2024-10-18 09:48:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":40807,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables28aug24.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4990604/v1/745f18c5ca0b4c53286e05ae.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personal traits, lifestyle decisions, and geography shape our dietary intake and consequently our bacterial and fungal gut microbiome","fulltext":[{"header":"Background","content":"\u003cp\u003eHabitual diet and geography have been suggested as among the strongest explanatory factors for human gut microbiota variation. A specific habitual diet may contribute to health and chronic conditions, such as obesity, metabolic syndrome, and inflammatory bowel disorders (IBD). These conditions and associated mortality/morbidity have risen dramatically over the past decades, with the gut microbiome implicated as one of the potentially causal human-environment interactions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2019, the Global Burden of Disease (GBD) Study assessed the impact of dietary habits on non-communicable diseases (NCDs) globally (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Using a comparative risk assessment approach, the researchers analyzed the consumption of major foods and nutrients across 195 countries. The findings revealed that in 2017, approximately 11\u0026nbsp;million deaths and 255\u0026nbsp;million disability-adjusted life-years (DALYs) were attributable to suboptimal dietary habits. High sodium intake, low intake of whole grains, and low intake of fruits were identified as the leading dietary risk factors for both deaths and DALYs worldwide. Overall, the research emphasizes the urgent need for improving dietary patterns globally to mitigate the burden of NCDs.\u003c/p\u003e \u003cp\u003ePrevious studies have identified significant variations in the gut microbial community among individuals, which has hindered the discovery of microbial species as reliable disease biomarkers. Various factors, including age, medication use, bowel habits, health status, anthropometric characteristics, habitual diet, and lifestyle, have been identified as potential contributors to this high microbiome variability (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Consequently, these variations necessitate a large cohort size to effectively discover and validate biomarkers.\u003c/p\u003e \u003cp\u003eOver the last decade, population studies have emerged to understand the role of habitual diets on health and disease. These large-scale studies, involving hundreds to thousands of participants, included both non-European countries such as the USA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), Canada (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and China (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), and European countries such as Belgium (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and the UK (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These studies exemplify large-scale projects that facilitate human microbiome hypothesis generation and testing on an unprecedented scale. They have uncovered associations between microbiome signatures and specific genetic variants, geographic variation, medication, and dietary habits.\u003c/p\u003e \u003cp\u003eAlthough the Spanish diet has been investigated in large-scale studies as part of the Mediterranean diet in relation to cardiovascular disease risk (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), no studies have yet comprehensively explored the association between the Spanish diet and both the gut bacterial and fungal microbiome using shotgun metagenomics at the population level.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to understand the relationship between diet and the microbiome to provide insights into how national nutritional recommendations can influence the microbial ecosystem and, consequently, human health. We collected personal and dietary data from a large population cohort, computed eating quality indexes from the dietary data, and correlated all collected data with microbiome data. Our findings demonstrated that lifestyle and demographic factors significantly influence specific dietary intake, which in turn affects bacterial and fungal microbiome composition and diversity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eParticipant\u0026rsquo;s recruitment.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e We conducted a prospective longitudinal study in accordance with the Declaration of Helsinki, approved by the local Ethics Committee of Vall d\u0026rsquo;Hebron University Hospital, Barcelona (PR(AG)84/2020). Participants were enrolled in the study between December 2020 and March 2024 through announcements on social platforms such as Facebook, LinkedIn and Instagram, as well as the Hospital Vall d\u0026rsquo;Hebron website. We recruited 1001 participants from different regions of Spain, aged 18 to 75, who had not taken antibiotics for at least three months and had no diagnosed chronic intestinal disorders, including inflammatory bowel diseases, type 2 diabetes, and autoimmune diseases, before entering the study. All participants signed a consent form.\u003c/p\u003e \u003cp\u003eTo calculate the sampling fraction for each region area, we first downloaded the relevant data from the Instituto Nacional de Estad\u0026iacute;stica (INE) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ine.es/jaxiT3/Tabla.htm?t=2853\u0026amp;L=0\u003c/span\u003e\u003cspan address=\"https://www.ine.es/jaxiT3/Tabla.htm?t=2853\u0026amp;L=0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) regarding the number of males and females between 18 and 75 years old in each autonomous community. We then calculated the population size for the selected region areas (Interior, North of Spain, Mediterranean and Islands) by summing up the individuals from the corresponding autonomous communities. Using these values, we estimated the theoretical percentage for a sample size of 1000 individuals as follows: Theoretical percentage = (1000 x Population in each region area)/Total population in Spain. To evaluate how accurately we achieved our recruitment goal, we divided the actual number of individuals recruited in each region area by the theoretical values. This resulted in a ratio ranging from 0 to 1, where a ratio closer to 1 indicates more accurate recruitment.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMetadata and sample collection.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eParticipants filled out an in-house validated short food frequency questionnaire (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), which provided demographic, lifestyle, clinical, and dietary data, and shipped their stool samples to the microbiome laboratory at baseline, month six, and month 12. The questionnaire was administered online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://manichanh.vhir.org/sFFQ/login.php\u003c/span\u003e\u003cspan address=\"https://manichanh.vhir.org/sFFQ/login.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, user:reviewers; password:reviewers). It included 58 food items divided into 13 sections (Supplementary Table S8): vegetables, legumes, and potatoes; fruits and dried fruits; cereals and derivatives; milk and derivatives; eggs, fish, and meat; selfish; oils and fats; bakery and pastry; sauces; non-alcoholic drinks; alcoholic drinks; processed food and others. Frequency of consumption was categorized into six possible options: \u0026ldquo;Never\u0026rdquo;, \u0026ldquo;1 or 3 times per month\u0026rdquo;, \u0026ldquo;1 or 2 times per week\u0026rdquo;, \u0026ldquo;3 or more times per week\u0026rdquo;, \u0026ldquo;once per day\u0026rdquo;, and \u0026ldquo;2 or more times per day\u0026rdquo;. Serving size consisted of a \u0026ldquo;standard portion\u0026rdquo; estimated using the ENALIA2 Survey (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) as well as our own expertise, \u0026ldquo;half of the standard\u0026rdquo; and \u0026ldquo;double of the standard\u0026rdquo;. To facilitate the estimation of the amount of food consumed by the participants, we added colored photographs. Additional information such as age, sex, weight, height, birth type, smoking, blood type, specific diet, consumption of ready-to-eat food or sweeteners, liquids and supplements, or medication was also recorded. The participants also self-collected their stool samples. The samples were preserved in 95% ethanol at room temperature and then shipped by the participants to the microbiome lab, where they were stored at -80\u0026ordm;C until DNA extraction.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDietary data processing.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe first step in converting the dietary information collected from the sFFQ was to transform monthly consumption into daily consumption: for instance, a consumption response of 1\u0026ndash;2 times per week was interpreted as an average consumption of 1\u0026middot;5 times per week, which, when divided by the seven days of the week, yielded an average daily consumption of 0.21. Subsequently, this consumption value was multiplied by the weight associated with the selected serving size. For instance, for the legume item with a serving size of 150 g and the aforementioned consumption frequency, the final value of grams per day would be 0.21 x 150 g\u0026thinsp;=\u0026thinsp;31\u0026middot;5 g/day. The values for the other consumption frequencies were as follows: 1\u0026ndash;3 times per month\u0026thinsp;=\u0026thinsp;0.066; +3 times per week\u0026thinsp;=\u0026thinsp;0.64; once per day\u0026thinsp;=\u0026thinsp;1; +2 times per day\u0026thinsp;=\u0026thinsp;3. Using this gram-per-day information, the energy and nutritional value of each item in the sFFQ were then calculated utilizing a custom-developed food composition database (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe calculated the magnitude of the influence of specific participant\u0026rsquo;s characteristics on dietary intake using permutational analysis of variance (PERMANOVA), as implemented in the adonis2 function of the vegan R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/vegan/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/vegan/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the Bray-Curtis method. The correlation between eating quality indexes and continuous population characteristics was calculated using the Spearman correlation test. For categorical data, the Mann-Whitney U test was used.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDietary indexes.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe utilized various eating quality indexes to assess the nutritional quality of diets. These indexes encompass the Healthy Eating Index-2015 (HEI-2015), the IASE (derived from its Spanish acronym '\u0026Iacute;ndice de Alimentaci\u0026oacute;n Saludable para la Poblaci\u0026oacute;n Espa\u0026ntilde;ola\u0026acute;), the plant-based dietary indexes PDI, uPDI (u\u0026thinsp;=\u0026thinsp;unhealthy), hPDI (h\u0026thinsp;=\u0026thinsp;healthy), the Healthy Food Diversity Index (HFD-index) and the Alternative Mediterranean Diet (aMED) score.\u003c/p\u003e \u003cp\u003eThe HEI-2015, developed by the United States Department of Agriculture (USDA), is a scoring system designed to provide recommended nutritional guidelines to promote health and prevent chronic diseases (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It assesses the intake of different food groups and nutrients, assigning scores to components such as fruits, vegetables, whole grains, dairy, protein foods, fatty acids, refined grains, sodium, added sugars, and saturated fats. Higher scores indicate better adherence to dietary guidelines, with the maximum score for each component representing optimal intake according to the guidelines.\u003c/p\u003e \u003cp\u003eThe IASE is a modified version of the HEI-2005, specifically tailored to assess the dietary quality of the Spanish population in 2011 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Similar to the HEI-2005, the IASE evaluates dietary patterns and adherence to dietary guidelines, but with considerations for the specific food choices and dietary habits commonly found in Spain. The IASE takes into account various components of the diet, including the consumption of fruits and vegetables, cereals and grains, proteins, dairy products, fats and oils, sweets, pastries, and alcoholic beverages. It assesses the quality of these food groups based on recommended intake levels and patterns that are more relevant to the Spanish diet and nutritional guidelines.\u003c/p\u003e \u003cp\u003eIntroduced by Satija et al. in 2017 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), the PDI, uPDI, and hPDI evaluate the quality of a person's diet based on various aspects of dietary intake in the US. The PDI assesses the proportion of plant-based foods consumed relative to animal-based foods. A higher PDI score indicates a diet richer in plant-based foods like fruits, vegetables, whole grains, nuts, and seeds, with lower consumption of animal-based foods such as meat and dairy. The uPDI focuses on less healthy plant-based items like refined grains, potatoes, and sweets, with a higher score suggesting an increased intake of these less nutritious plant-based foods. In contrast, the hPDI emphasizes the consumption of healthier plant-based foods within a plant-based diet, such as fruits, vegetables, whole grains, nuts, and legumes, with a higher hPDI score reflecting a diet rich in these nutrient-dense plant-based food groups.\u003c/p\u003e \u003cp\u003eThe HFD, developed by Dresher et al. in 2007 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), measures food intake diversity by evaluating the intake of various food groups including fruits, vegetables, whole grains, lean proteins, and healthy fats. A higher HFD-index score generally indicates a more diverse and nutritious diet.\u003c/p\u003e \u003cp\u003eThe aMED score corresponds to a scoring system developed by Fung et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which is based on the original Mediterranean diet scale proposed by Trichopoulou et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The aMED score ranges from 0 (indicating minimal adherence) to 9 (representing perfect adherence) points and evaluates adherence to nine food groups: 1) All kinds of vegetables excluding potatoes; 2) Legumes including tofu, beans, and peas; 3) Fruits and fruit juices; 4) Nuts including peanut butter; 5) Whole grains; 6) Red and processed meat; 7) Fish and shellfish; 8) Ratio of monounsaturated to saturated fat; 9) Alcoholic drinks. For each category, including the fatty acid ratio, the median intake was calculated in grams per day. Healthy food groups (vegetables, legumes, fruits, nuts, whole grains, fish, and the fatty acid ratio) were scored with 1 if the participant's intake was above the median and 0 if it was below. Conversely, for red and processed meats, 1 point was assigned if participants reported lower intake compared to the median, while 0 points were given for higher intake. Alcoholic drinks were scored differently. For men, consumption between 10\u0026ndash;50 grams per day or 5\u0026ndash;25 grams per day for females received 1 point, while intake outside these ranges received a score of 0.\u003c/p\u003e \u003cp\u003eComparison of dietary intake with recommendations from the GBD-2017 consortium.\u003c/p\u003e \u003cp\u003eTo compare major food and nutrient consumption within the context of the Global Burden of Disease (GBD) study, we grouped our semi-quantitative sFFQ items into 12 out of the 15 proposed dietary risk factors defined by the GBD, aiming to align with their dietary profiles. We calculated the median intake in grams per day for fruits, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, sugar-sweetened beverages, fiber, and calcium, and compared these values with the optimal and optimal range of intake defined in the GBD study. For polyunsaturated fatty acids (PUFAs), we calculated their consumption percentage relative to the total energy intake and compared it with the GBD recommended values. Sodium was omitted from our analysis as our data only reflected sodium present in food and did not account for sodium added during cooking. Additionally, seafood omega-3 and trans fatty acids were not evaluated due to the absence of these variables in our sFFQ. Supplementary Table S3 listed the clustering of items into the dietary risk factors as suggested by the GBD consortium.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMicrobiome analysis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDNA extraction.\u003c/b\u003e An in-house protocol was used to perform extraction of the genomic DNA from stool samples. This protocol contains a beat beating step to break-down microbial cell walls. A frozen aliquot (200 mg) was extracted as described in our previous paper (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiome sequencing.\u003c/b\u003e The DNA shotgun library was prepared and sequenced using the Illumina NovaSeq6000 platform. The sequencing process provided an average of 5 Gb of sequence data per sample. The KneadData v0.7.4 pipeline was used to pre-process and decontaminate the sequence reads (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://huttenhower.sph.harvard.edu/kneaddata\u003c/span\u003e\u003cspan address=\"https://huttenhower.sph.harvard.edu/kneaddata\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KneadData performed a quality filtering of the reads using trimmomatic and then mapped them against a human reference genome database using Bowtie 2. Reads with lengths below 50% of the total input length and also those that mapped with the human genome were discarded from further analysis. Taxonomic profiles were provided by the MetaPhlan4\u0026rsquo;s intermediary output file in the HumanN3 pipeline and functional profiles from the final output (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Taxonomic profiles, the outputs of MetaPhlan4, were generated in stratified relative abundance, from phylum to SGB level. For this reason, no normalization was applied, but the stratified relative abundances were extracted according to the taxonomic species level. Alpha and beta diversity analyses were performed using Chao1 and Shannon indexes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and the adonis2 function (Permutational Multivariate Analysis of Variance), respectively.\u003c/p\u003e \u003cp\u003eFunctional profiles, the output of HumanN3, provided gene families and MetaCyc pathways. MetaCyc pathways were filtered to remove unmapped and unintegrated reads. All features that did not achieve 0.001 abundance and 0.1 prevalence (pathways that did not achieve 0.1% of the total sample abundance in at least 10% of the samples) were also discarded. Then, pathways were sum-normalized to counts per million (CPM) before further analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analyses for microbiome sequence data\u003c/b\u003e were performed in R (v4.3). Covariates such as gender, age, body mass index (BMI), region areas, smoking habit, season, and workplace were tested for their impact on microbiota variation using the PERMANOVA test on weighted and unweighted UniFrac distance indexes.\u003c/p\u003e \u003cp\u003eWe assessed the ability of the microbiome to predict distinct food items, food groups, and nutrients using the random forest classification and regression algorithms (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For both the regression and classification processes, a cross-validation approach was used based on 100 bootstrap iterations and an 80/20 random split of training and testing folds. For the classification task, item, food group, and nutrient frequencies were divided into the first and fourth quartile, used as the two classes to predict. Both regression and classification algorithms were trained on species-level genome bin (SGB)-level features as estimated by MetaPhlAn4. Classification was evaluated using the median AUC, while regression used the median Spearman\u0026rsquo;s correlation between the actual and predicted values.\u003c/p\u003e \u003cp\u003eGiven the compositional nature of the sequence data, differential abundance (DA) analysis of the microbial community was performed using MaAsLin2 (Multivariate Association with Linear Models) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The analysis tested for differences in population characteristics while including gender, BMI, and age as confounding factors. The resulting p-values were corrected for false discovery rate (FDR). Associations identified by MaAsLin2 were considered significant if the coefficient, measuring the strength and direction of association, was greater than 1 (in most cases) and the q-value was less than 0.05. Spearman tests were used to correlate dietary data with microbiome data.\u003c/p\u003e \u003cp\u003eFor functional analysis, Spearman's correlation between alpha diversity indexes (Chao1 and Shannon) and pathway abundances were computed and FDR corrected. Correlations with \u0026minus;\u0026thinsp;0.4\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;rho\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.4 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant and kept for further analyses. Association analysis was performed between these pathways and food items, food groups, and nutrients using the Spearman correlation test.\u003c/p\u003e \u003cp\u003eTo assess changes in the potential pathways of the microbial community depending on personal information, we used linear models as implemented in MaAsLin2, adjusting for bowel movement (transit time), gender, BMI, age, smoking habit, region area, and season years as fixed-effects, using MetaCyc pathways information. To increase the interpretability of these results, pathways were grouped into their MetaCyc parent instances up to 7 levels, in which each level represents broader biological function, with level 1 being the broadest and 7 the most specific. Pathways with more than one parent instance were duplicated and assigned to different parents for plotting and interpretation purposes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFungal enrichment procedure, sequencing, and statistical analysis\u003c/h2\u003e \u003cp\u003eTo improve the detection of fungal species, a subset of 100 samples was selected to apply the fungal enrichment protocol described by Xie et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The fungal partition was processed for genomic DNA extraction and shotgun metagenomic sequencing, as previously described, while the bacterial partition was discarded. Fungal profiling was performed on both the 99 enriched samples (1 sample failed during the enrichment procedure) and the 500 non-enriched samples. We used KneadData v0.7.4 for read quality control and decontaminating human sequences. Then, FunOMIC2 database and pipeline were used to obtain the taxonomic and functional fungal profiles.\u003c/p\u003e \u003cp\u003eRaw counts were normalized with the CPM method implemented in the \u0026ldquo;edgeR\u0026rdquo; R package. Fungal alpha diversity was assessed by computing Chao1 index on raw fungal species counts, and Shannon index on CPM-normalized counts. Beta diversity was assessed by computing Bray-Curtis distances. To compare samples with and without fungal enrichment, total annotated fungal reads and alpha diversity were tested for significant differences with a paired Wilcoxon test. Beta diversity was compared with the Adonis PERMANOVA test. Further analyses on fungal composition were performed on the 99 enriched samples. Alpha diversity was compared between gender, smoking habit, region, and season groups with the Mann-Whitney U test and was correlated with the Spearman correlation test with age and BMI, diet indexes, food items, food groups, and nutrients. Spearman correlation was also used to find associations between fungal species and dietary information. The p-values were adjusted using the Benjamini-Hochberg method. We considered q-values (adjusted p-values) significant when \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWebsite construction\u003c/h2\u003e \u003cp\u003eWe built a website dedicated to this study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://manichanh.vhir.org/POP/\u003c/span\u003e\u003cspan address=\"https://manichanh.vhir.org/POP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eusername:reviewers, password:reviewers\u003c/span\u003e), where participants can access an overview of the results of this research, as well as their personal information on nutrient intake and dietary indices (based on the short food frequency questionnaire), and, if available, their microbiome sequencing results, including bacterial and fungal composition, and measures of alpha diversity. Nutrient intake data are compared to the guidelines established by the Scientific Committee of the Spanish Agency for Food Safety and Nutrition (AESAN), while dietary indices and alpha diversity scores are compared to the population median found in this study.\u003c/p\u003e \u003cp\u003eParticipant reports are produced dynamically in the form of a Shiny app (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://shiny.posit.co/\u003c/span\u003e\u003cspan address=\"https://shiny.posit.co/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which is run in R language and hosted in our local Shiny server. All personal results are anonymized and password-protected, ensuring each participant may only access their own information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCohort characteristics and collected metadata and samples\u003c/h2\u003e \u003cp\u003eBetween 2020 and 2024, we enrolled 1,001 participants from four regions in Spain, covering all 17 autonomous communities (Fig.\u0026nbsp;1AB). The cohort consisted of 458 men and 542 women, all over 18 years old. None of the participants had taken antibiotics for at least three months before the study began, and none had any diagnosed chronic intestinal disorders. Further details regarding the cohort's characteristics can be found in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. We employed an in-house (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) online short Food Frequency Questionnaire (sFFQ) to gather demographics, biometrics, lifestyle, and dietary data. Participants filled out the sFFQ at baseline (n\u0026thinsp;=\u0026thinsp;1001), month six (n\u0026thinsp;=\u0026thinsp;822), and month 12 (n\u0026thinsp;=\u0026thinsp;652), resulting in a total of 2475 completed sFFQs. Additionally, at baseline, stool samples (n\u0026thinsp;=\u0026thinsp;500) were collected concurrently with the sFFQ for comprehensive analysis. These samples underwent microbiome compositional and functional profiling through shotgun sequencing (Fig.\u0026nbsp;1CD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePersonal traits, lifestyle decisions, and geography influence the quality of dietary intake (n = 1001)\u003c/h3\u003e\n\u003cp\u003eThe collected 58 food items, from 2475 sFFQs, were categorized into 24 food groups and 32 macro- and micronutrient contents (refer to the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section). We then investigated the relationship between covariates such as lifestyle, biometrics, and demographic factors on dietary intake using the adonis method, also known as Permutational Multivariate Analysis of Variance (PERMANOVA). These self-reported covariates included age, geography, workplace (hospital or non-hospital), gender, BMI, season, dietary types, smoking status, sweetener consumption, menstruation or menopause status (if applicable), and bowel habits. All covariates, except for workplace, were found to be significantly associated with the composition of food items and food groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, seven of these covariates (region areas, gender, season, dietary types, smoking status, sweetener consumption, and bowel habits) were associated with variations in macro- and micronutrient intake (PERMANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTaking advantage of the longitudinal setting of the study, we analyzed the intra- and inter-variability of food intake using the Bray-Curtis similarity index for food items, food groups, and nutrient data. As expected, we found that intra-individual variability (with sFFQs analyzed six months apart) was lower than inter-individual variability across all three dietary classifications (p\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026middot;2 x 10^-16, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This suggests a relatively stable intra-individual dietary pattern across different seasons at all dietary levels.\u003c/p\u003e \u003cp\u003eNext, we examined how differences in population characteristics may explain variances in several eating quality indexes (EQIs), which were developed based on well-established national guidelines to evaluate the nutritional quality of individuals' diets and their adherence to recommended dietary patterns (refer to the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section for comprehensive explanations). To achieve this, we initially utilized the collected food items, food groups, and nutrients to calculate various EQIs (HEI-2015, IASE, HFD, hPDI, uPDI, and the aMED). Subsequently, we employed linear regression models, implemented in MaAsLin2, to assess the impact of different population characteristics on these EQIs while controlling for potential covariates mentioned above. Increasing age was found positively associated with a healthier diet as indicated by two EQIs (q(IASE)\u0026thinsp;=\u0026thinsp;0.03; q(hPDI)\u0026thinsp;=\u0026thinsp;7\u0026middot;1x10^-07) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and with several food groups such as alcoholic beverages, whole bread, nuts and seeds, fruits, and fruit products (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMen exhibited lower values of IASE, hDPI, aMED, and HFD, and higher values of uDPI compared to women, indicating poorer dietary habits among men (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Men's dietary patterns were more associated with the consumption of ready-to-eat meals (q\u0026thinsp;=\u0026thinsp;0.038) and alcoholic beverages (q\u0026thinsp;=\u0026thinsp;0.00014), whereas women showed higher consumption of whole bread (q\u0026thinsp;=\u0026thinsp;0.013), vegetables (q\u0026thinsp;=\u0026thinsp;5x10^-09), nonalcoholic drinks (q\u0026thinsp;=\u0026thinsp;0.0019), fruits and fruit products (q\u0026thinsp;=\u0026thinsp;0.002), fish and shellfish (q\u0026thinsp;=\u0026thinsp;0.00024), but also higher intake of fats and oils (q\u0026thinsp;=\u0026thinsp;2\u0026middot;7x10^-07) (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeographically, we divided Spain into four regional areas: the Mediterranean, the Interior, the North, and the Islands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This classification considers traditional Mediterranean diet patterns, geographical distribution, and socio-economic factors, all of which can influence dietary habits and patterns (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Compared to the Mediterranean region, the Interior exhibited a healthier dietary pattern based on the three eating quality indices (aMED, uPDI, HEI_2015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), characterized by a higher consumption of legumes (q\u0026thinsp;=\u0026thinsp;0.013, Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInteresting positive associations were identified between population behaviors and specific food groups (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For instance, the use of sweeteners was correlated with the consumption of sugar (q\u0026thinsp;=\u0026thinsp;0.044), ready-to-eat meals (q\u0026thinsp;=\u0026thinsp;0.000018), sauces and condiments (q\u0026thinsp;=\u0026thinsp;0.00042), and sausages and other meat products (q\u0026thinsp;=\u0026thinsp;0.018). Additionally, smoking (q\u0026thinsp;=\u0026thinsp;0.0011) or past smoking (q\u0026thinsp;=\u0026thinsp;0.0031) habits were associated with alcohol consumption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePartial alignment with recommendations from the GBD-2017\u003c/h2\u003e \u003cp\u003eTo evaluate whether the dietary intake of our population aligned with the recommendations of the Global Burden of Disease (GBD) Study 2017 (GBD, 2019, PMID: 30954305), we categorized our 58 sFFQ items (n\u0026thinsp;=\u0026thinsp;1001; 2475 sFFQs) into 12 of the 15 GBD dietary risk factors (refer to the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003emethods\u003c/span\u003e section, Supplementary Table S3). Our cohort's intake of fruits, vegetables, and fiber met the recommended ranges set by the GBD study (Supplementary Table S4). However, we observed suboptimal intake levels for legumes, polyunsaturated fatty acids (PUFA), whole grains, nuts, milk, and calcium compared to GBD recommendations. Additionally, the intake of red meat, processed meat, and sugar-sweetened beverages exceeded the levels recommended by the GBD guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic, anthropometric, and dietary data correlate with bacterial microbiome data\u003c/h2\u003e \u003cp\u003eNext, to assess the effect size of population characteristics on the microbiome, we employed the adonis2 method based on Bray-Curtis distances of microbiome sequence data. Specifically, gender, age, and BMI demonstrated significant impacts on microbiome composition at the global level (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These covariates were subsequently used as possible confounders in downstream association analysis.\u003c/p\u003e \u003cp\u003eAlthough there is no definitive evidence in the literature establishing a direct link between high gut microbial diversity and healthy status, several disorders, including inflammatory bowel diseases (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), obesity (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), and diabetes (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), have consistently been associated with low microbial diversity. These associations suggest that a diverse gut microbiome plays a role in maintaining health. Using the Spearman test, we assessed the correlation between population characteristics, dietary data, and microbiome diversity. The results showed that diversity (based on the Shannon index) was positively associated with vegetable intake (rho\u0026thinsp;=\u0026thinsp;0.12, q\u0026thinsp;=\u0026thinsp;0.0088), fruits (rho\u0026thinsp;=\u0026thinsp;0.15, q\u0026thinsp;=\u0026thinsp;0.0019), fiber intake (rho\u0026thinsp;=\u0026thinsp;0.1, q\u0026thinsp;=\u0026thinsp;0.026), and nuts and seeds (rho\u0026thinsp;=\u0026thinsp;0.12, q\u0026thinsp;=\u0026thinsp;0.018), while white bread was negatively linked to microbial diversity (rho=-0.15, q\u0026thinsp;=\u0026thinsp;0.009) and white grains (rho=-0.16, q\u0026thinsp;=\u0026thinsp;0.00304) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This is further supported by the positive correlations between the Shannon index and dietary indexes such as the HEI-2015 (rho\u0026thinsp;=\u0026thinsp;0.13, q\u0026thinsp;=\u0026thinsp;0.0031), the hPDI (rho\u0026thinsp;=\u0026thinsp;0.13, q\u0026thinsp;=\u0026thinsp;0.0026), the aMED index (rho\u0026thinsp;=\u0026thinsp;0.13, q\u0026thinsp;=\u0026thinsp;0.0047), which emphasize fruit and vegetable consumption, while the uPDI was negatively correlated with diversity (rho=-0.14, p\u0026thinsp;=\u0026thinsp;0.0016) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results suggest that adherence to national dietary guidelines and recommendations was associated with increased microbial diversity.\u003c/p\u003e \u003cp\u003eAdditionally, diversity (Shannon index, rho\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.006) and richness (Chao1 index, rho\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;=\u0026thinsp;0.007) positively correlated with age, reinforcing the connection between older age and healthier eating habits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the contrary, BMI (rho=-0.11, p\u0026thinsp;=\u0026thinsp;0.011), and uPDI (rho=-0.11, p\u0026thinsp;=\u0026thinsp;0.01) index were found negatively correlated with both richness and diversity. Given that age was also associated with BMI (rho\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), these findings suggest that higher diversity is linked to older age and lower BMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also observed a seasonal effect on dietary intake and microbiome diversity, with higher bacterial diversity in summer compared to winter (p\u0026thinsp;=\u0026thinsp;0.048), which could be due to a higher consumption of fruits and vegetables in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Association analysis between pathways and dietary data revealed significant correlations between the L-arginine biosynthesis II and sucrose biosynthesis II pathways and the consumption of fruits, nuts, and seeds. At the nutrient level, significant associations were also found with fiber intake (Supplementary Table S5). These findings suggest that diet can influence not only the composition of the gut microbiome but also its functional capabilities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe extent to which transit time (bowel movement) influences the microbiome is still not well understood. To address the question related to the impact of transit time on the microbiome community, we examined the association between defecation frequencies obtained from the sFFQs (categorized as 1\u0026middot;5 times/week, \u0026gt;\u0026thinsp;3 times/week, 1 time/day, 2 times/day, and \u0026gt;\u0026thinsp;2 times/day) on microbiome diversity and composition using the Wilcoxon test and general linear models (MaAsLin2), respectively. Our results indicated that longer transit times were associated with higher diversity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, we observed that microbiome diversity appeared to stabilize at a defecation frequency of more than 3 times per week, as indicated by non-significant differences in the Chao1 and Shannon indexes between defecating more than 3 times per week and 1\u0026middot;.5 times per week. At the compositional level, using one defecation per day as a reference, 20 bacterial species (including \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e) were positively associated, while three species (including \u003cem\u003eLacrimispora amygdalina\u003c/em\u003e and \u003cem\u003eBlautia wexlerae\u003c/em\u003e) were negatively associated with longer transit times (more than three times and only 1\u0026middot;5 times per week). Conversely, one species (\u003cem\u003eRuthenibacterium lactatiformans\u003c/em\u003e) was negatively associated with short transit times (\u0026gt;\u0026thinsp;2 times per day). (Supplementary Table S6). At the functional level, longer transit times were associated with more pathways than lower transit times. These pathways include fermentation, glycan, amine degradation, amino acids degradation and biosynthesis, and lipid biosynthesis, while shorter transit times were more linked to carbohydrate degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Other correlations were found between microbiome and demographic and biometric data including age, BMI, gender, season, and smoking (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table S7). Interestingly, BMI, which correlated with three bacterial species, also correlated with 39 pathways (26 positive and 13 negative correlations).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with fungal microbiome\u003c/h2\u003e \u003cp\u003eUsing our standard extraction protocol optimized for bacterial DNA recovery, we detected fungal reads in only 340 out of 500 fecal samples. Additionally, the number of fungal reads recovered was extremely low, with a median of just 2 reads per sample, which is insufficient for proper mycobiome characterization. Therefore, we performed an enrichment procedure using an in-house method, as described in Xie et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), on a randomly selected subset of 100 samples, matched for gender. Two samples did not pass the quality control test during the library construction step. Fungal reads were then detected in all the 98 remaining samples with a median of 25 reads per sample, compared to 73 without the enrichment protocol. A total of 141 different species were detected in the enriched samples, compared to 45 species in the non-enriched samples. We also observed a significant increase in alpha diversity based on Chao1 and Shannon indexes with the enrichment protocol (Supplementary Figure S3). The top five most prevalent species in the enriched samples were \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e (80 samples), \u003cem\u003eMalassezia restricta\u003c/em\u003e (33 samples), \u003cem\u003eDebaryomyces hansenii\u003c/em\u003e (25 samples), \u003cem\u003ePenicillium roqueforti\u003c/em\u003e (21 samples) and \u003cem\u003eMeira nashicola\u003c/em\u003e (21 samples).\u003c/p\u003e \u003cp\u003eSimilar to the bacterial microbiome, we evaluated the association between population characteristics, dietary intake, and the fungal microbiome diversity, and composition. The few significant correlations found included age, seasons, and the uPDI index. Fungal diversity increased with age (rho(Shannon)\u0026thinsp;=\u0026thinsp;0.2, p\u0026thinsp;=\u0026thinsp;0.044) and was higher in spring/summer compared to autumn/winter (p(Chao1)\u0026thinsp;=\u0026thinsp;0.022; p(Shannon)\u0026thinsp;=\u0026thinsp;0.0056). This diversity decreased with the uPDI index (rho(Shannon) =-0.26, p\u0026thinsp;=\u0026thinsp;0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No strong association was found between fungi and dietary data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of dietary intake by the gut microbiome\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;GBD 2017 Diet Collaborators\u0026rdquo; reported in 2019 that high intake of sodium, and low intake of whole grains and fruits were the leading dietary risk factors for deaths and years of life adjusted for disability (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In our study, sodium was not properly evaluated in the questionnaire, as we did not add any specific question related to the added sodium during the cooking process, therefore we cannot assess the impact of salt on the microbiome. However, using the machine learning approach to microbiome features and the reported dietary data, we showed that several food items were strongly associated with microbiome composition. These food items included coffee with and without caffeine (rho\u0026thinsp;=\u0026thinsp;0.41, AUC\u0026thinsp;=\u0026thinsp;0.82), nuts and seeds (rho\u0026thinsp;=\u0026thinsp;0.25, AUC\u0026thinsp;=\u0026thinsp;0.76,), vegetables (rho\u0026thinsp;=\u0026thinsp;0.19, AUC\u0026thinsp;=\u0026thinsp;0.67), fruits (rho\u0026thinsp;=\u0026thinsp;0.19, AUC\u0026thinsp;=\u0026thinsp;0.66), fermented dairy (rho\u0026thinsp;=\u0026thinsp;0.18, AUC\u0026thinsp;=\u0026thinsp;0.74), and dark chocolate (rho\u0026thinsp;=\u0026thinsp;0.18, AUC\u0026thinsp;=\u0026thinsp;0.66). The analysis using food groups validated the findings with nuts and seeds (rho\u0026thinsp;=\u0026thinsp;0.24, AUC\u0026thinsp;=\u0026thinsp;0.75), fruits (rho\u0026thinsp;=\u0026thinsp;0.20, AUC\u0026thinsp;=\u0026thinsp;0.68), milk and dairy (rho\u0026thinsp;=\u0026thinsp;0.20, AUC\u0026thinsp;=\u0026thinsp;0.65), vegetables (rho\u0026thinsp;=\u0026thinsp;0.19, AUC\u0026thinsp;=\u0026thinsp;0.67), yogurt (rho\u0026thinsp;=\u0026thinsp;0.17, AUC\u0026thinsp;=\u0026thinsp;0.73), and chocolates (rho\u0026thinsp;=\u0026thinsp;0.16, AUC\u0026thinsp;=\u0026thinsp;0.66) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing Spearman correlation test, coffee was found associated with five bacterial species: \u003cem\u003eClostridium phoceensis\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.4, q\u0026thinsp;=\u0026thinsp;0), \u003cem\u003eMassilioclostridium coli\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.34, q\u0026thinsp;=\u0026thinsp;4\u0026middot;7x10^-12), \u003cem\u003eClostridium bacterium_12CBH8\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.26, q\u0026thinsp;=\u0026thinsp;20.9x10^-06), two unannotated bacterial species: GGB9494_SGB14891, GGB9557_SGB14966 (rho\u0026thinsp;=\u0026thinsp;0.25, q\u0026thinsp;=\u0026thinsp;2\u0026middot;9x10^-06). Nuts and seeds were found linked to three annotated bacterial species: \u003cem\u003eLachnospiraceae bacterium\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.23, q\u0026thinsp;=\u0026thinsp;0.0002), \u003cem\u003eFlavonifractor plautii\u003c/em\u003e (rho=-0.19, q\u0026thinsp;=\u0026thinsp;0.014), and \u003cem\u003eRoseburia hominis\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.18, q\u0026thinsp;=\u0026thinsp;0.18) and one unannotated species (GGB3478_SGB4643, rho\u0026thinsp;=\u0026thinsp;0.22, q\u0026thinsp;=\u0026thinsp;0.0006). Vegetables were positively associated with three unannotated bacterial species (Clostridium_sp AF20_17LB (rho\u0026thinsp;=\u0026thinsp;0.2), Bacilli unclassified SGB6422 (rho\u0026thinsp;=\u0026thinsp;0.18, Lachnospiraceae unclassified SGB5063, rho\u0026thinsp;=\u0026thinsp;0.18)) and negatively associated with \u003cem\u003eFlavonifractor plautii\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.19, q\u0026thinsp;=\u0026thinsp;0.014). Fruits were positively correlated with two unknown species (GGB9758_SGB15368, GGB4676_SGB6465), \u003cem\u003eLachnospira eligens\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.19, q\u0026thinsp;=\u0026thinsp;0.011) and an unclassified Bacilli (SGB6473, rho\u0026thinsp;=\u0026thinsp;0.18, q\u0026thinsp;=\u0026thinsp;0.013). Fermented dairies including yogurt and kefir were associated with \u003cem\u003eStreptococcus thermophilus\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.32, q\u0026thinsp;=\u0026thinsp;5\u0026middot;710^-10) and to a lesser extent with \u003cem\u003eBifidobacterium animalis\u003c/em\u003e (rho\u0026thinsp;=\u0026thinsp;0.17, q\u0026thinsp;=\u0026thinsp;0.11). Chocolate (\u0026gt;\u0026thinsp;50% cacao) was positively associated with Clostridium_sp_AF32_12BH (rho\u0026thinsp;=\u0026thinsp;0.18, q\u0026thinsp;=\u0026thinsp;0.018) and two unclassified bacterial species (GGB52930_SGB73859, rho\u0026thinsp;=\u0026thinsp;0.20, q\u0026thinsp;=\u0026thinsp;0.006; GGB3478_SGB4643, rho\u0026thinsp;=\u0026thinsp;0.19, q\u0026thinsp;=\u0026thinsp;0.010). No strong prediction could be recovered from fungal sequence data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWebsite for the Citizen science project\u003c/h2\u003e \u003cp\u003eThis project was designed to engage the public in data collection and raise awareness about scientific research. Participants contributed by providing their dietary data and stool samples. Through the website created for this project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://manichanh.vhir.org/POP/\u003c/span\u003e\u003cspan address=\"https://manichanh.vhir.org/POP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, username:reviewers, password:reviewers), participants were able to collect their dietary information using the sFFQ and ship their stool samples to the microbiome lab. Participants learned about the overall study findings and accessed their personal dietary and microbiome profiles. The website also offers resources to help participants understand the significance of their contributions and the impact of the research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study uncovered new insights into the complex interplay between EQIs, personal traits, lifestyle choices, geography, and diet, and their impact on the gut microbial community, revealing how national dietary recommendations can influence this community.\u003c/p\u003e \u003cp\u003eEQIs have been developed to serve as comprehensive tools for evaluating diet quality and guiding dietary recommendations. Researchers use EQIs to facilitate research on how diet affects the risk of chronic diseases, such as obesity, diabetes, cardiovascular diseases, and certain cancers (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In the present study, the assessment of the impact of the population characteristics on the nutritional quality revealed crucial insights into how age, gender, geographical location, and lifestyle shape eating habits. Our findings, reporting healthier dietary habits as we age, are validating previous works that showed that older adults have a more \u0026ldquo;prudent\u0026rdquo; dietary pattern characterized by higher intakes of vegetables, fruits, whole grains, nuts and seeds (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In our study, we excluded individuals older than 75 years to avoid potential confounding factors, such as age-related undiagnosed diseases like frailty or early stage neurodisorders.\u003c/p\u003e \u003cp\u003eThrough the analysis of self-reported defecation frequency, we showed transit time is a significant factor influencing gut microbiome diversity and composition. Longer transit times, associated with higher microbial diversity, could potentially support gut health by enabling a more resilient and varied microbial ecosystem. Conversely, very rapid transit times (\u0026gt;\u0026thinsp;2 times per day) might limit microbial diversity and favor certain species over others, potentially impacting overall gut health. The stabilization of diversity at moderate defecation frequencies indicates a potential balance point that might be optimal for maintaining a healthy microbiome. Our results validate the work from Asnicar et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) assessing the relationship between gut transit time and the human gut microbiome, using the blue dye method. They also reported that longer gut transit time was associated with higher bacterial diversity and specific microbial species.\u003c/p\u003e \u003cp\u003eUsing a machine learning approach, the study identified key food items and food groups strongly associated with microbiome composition. Coffee, nuts and seeds, vegetables, fruits, fermented dairy, and dark chocolate emerge as significant predictors of microbial composition. As a Mediterranean country, Spain's traditional diet is rich in fruits, legumes, whole grain cereals, vegetables, nuts, and healthy unsaturated fats primarily from olive oil. It also includes frequent fish intake, moderate consumption of dairy products and fermented beverages, and a low intake of meat and meat-derived products (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Despite its benefits, adherence to the Mediterranean diet (MD) in Spain has decreased over time, shifting towards a more Western dietary pattern (\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe influence of regional dietary habits, particularly within Mediterranean countries, is well-known. Our study\u0026rsquo;s division of Spain into the Mediterranean, Interior, North, and Islands, and its identification of healthier dietary patterns in the Interior region, aligns partially with prior research showing geographical variability in adherence to the Mediterranean diet and other dietary patterns (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Moreover, our study showed that individuals from the Interior region were characterized by higher consumption of legumes, which offer a range of health benefits due to their rich nutrient content and bioactive compounds including protein, fiber, vitamins, and minerals.\u003c/p\u003e \u003cp\u003eAmong the dietary variables proposed by the Global Burden of Disease study, our Spanish cohort complied with only 3 out of the 12 food groups analyzed: vegetables (321\u0026middot;48 g/day), fruits (225\u0026middot;6 g/day), and fiber (27\u0026middot;32 g/day). These three groups were related to higher alpha diversity and correlated with bacterial species with potential health implications. For instance, vegetables were negatively correlated with \u003cem\u003eFlavonifractor plautii\u003c/em\u003e, a flavonoid-degrading bacterium associated with less healthy diets, lower scores in EQIs, and related to disease outcomes such as colorectal cancer, inflammatory bowel disease (IBD), depression, and bipolar disorder.\u003c/p\u003e \u003cp\u003eThe association analysis of food group consumption reveals gender-specific dietary behaviors. It is recognized that women generally exhibit healthier dietary patterns than men, consuming more fruits, vegetables, and whole grains, while men consume more meat and alcohol (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These findings are validated by our study, which shows that men have a higher consumption of ready-to-eat meals and alcoholic beverages.\u003c/p\u003e \u003cp\u003eLow bacterial diversity has been linked to various disorders, suggesting a connection between health status and high microbial diversity (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The present study demonstrates that adherence to national dietary guidelines\u0026mdash;particularly increased consumption of fruits, vegetables, fiber, nuts, and seeds\u0026mdash;positively correlated with microbial alpha diversity. Conversely, adherence to an unhealthy diet with a high intake of white bread negatively affects microbial richness and diversity. These findings align with previous reports indicating that a high-fiber diet enhances alpha diversity, while a low-fiber diet, such as one high in white bread, reduces it (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key component of this project was the development of a website, which allowed participants to efficiently collect and submit their dietary information using a structured Food Frequency Questionnaire (sFFQ). Beyond data collection, the website provided participants with private access to both the overall study findings and their personalized dietary and microbiome profiles, enhancing their understanding of their contributions.\u003c/p\u003e \u003cp\u003eAdditionally, we ensured that the website offered comprehensive resources to help participants appreciate the significance of their involvement and the broader impact of the research. This integrated approach not only facilitated data collection but also strengthened the connection between the participants and the scientific community.\u003c/p\u003e \u003cp\u003eAnalyzing only 500 samples allowed us to uncover similar results to previous studies with larger sample sizes, such as the association between vegetables, fruits, transit time, and diversity, and the role of coffee as a main factor influencing microbiome composition (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Despite our efforts to achieve a similar sampling fraction for each of the Spanish regions, our results may be biased due to an over-recruitment of participants from the Mediterranean region. However, to limit the effect of regional differences in the results, region areas were considered as a covariate in the statistical models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e This study highlights the significant influence of personal traits, lifestyle choices, geography, and dietary habits on the gut microbiome, and underscores the importance of promoting national dietary guidelines to enhance gut microbial diversity and improve health outcomes, emphasizing the need for continued adherence to the Mediterranean diet amidst shifting dietary patterns in Spain. Future research could explore longitudinal studies to further elucidate causal relationships between dietary patterns, microbiome composition, and health outcomes, ultimately paving the way for precision medicine approaches in nutrition and healthcare.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local Ethics Committee of the Vall d\u0026rsquo;Hebron University Hospital, Barcelona (Project identification code: PR(AG)84/2020). All participants signed a consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants consented independently when donating samples. All data obtained and generated during the study were kept confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Instituto de Salud Carlos III/FEDER (PI20/00130; FI21/00262). Marc Pons and Sara Vega-Abellaneda were supported by the AGAUR (2021 SGR 00459). Francisca Y\u0026aacute;\u0026ntilde;ez was supported by a fellowship from ANID, BECAS Chile, No. 72190278.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collected for the study include individual participant data and microbiome sequence data. Participants were codified. Upon publication shotgun metagenomic sequencing raw data (short-read archives, SRA) will be made available via NCBI Project Number PRJNA1146994. Any additional information needed to reanalyze the data reported in this work is available upon request from the corresponding author of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.S. contributed to literature searches, data collection, data analysis, data interpretation, writing, review, and editing. M. P.-T., I. M., C. C., E. V., and F. Y. contributed to data curation, sample processing, review, and editing. G. S.-G., S. V.-A., M. R.-B., Z. X., A. N.-S. contributed to bioinformatics analysis and website building, review \u0026amp; editing. C. M. contributed to study design, fundraising, conceptualisation, data analysis, data interpretation, writing, review, and editing. All authors are from the academic team. Z. S. and C. M. had accessed and verified the data reported in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDIBD. The global, regional, and national burden of inflammatory bowel disease in 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol 2020;5(1):17\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GBDD. Health effects of dietary risks in 195 countries, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019;393(10184):1958\u0026ndash;1972.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y, Faust K et al. Population-level analysis of gut microbiome variation. 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Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol 2017;2:17004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePascal V, Pozuelo M, Borruel N, Casellas F, Campos D, Santiago A et al. A microbial signature for Crohn's disease. Gut 2017;66(5):813\u0026ndash;822.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013;500(7464):541\u0026ndash;546.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 2016;535(7612):376\u0026ndash;381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang X, Liu J, Zhu Z, Zhang X, Huang Y, Liu S et al. Healthy dietary patterns and the risk of individual chronic diseases in community-dwelling adults. Nat Commun 2023;14(1):6704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiza HA, Casavale KO, Guenther PM, Davis CA. Diet quality of Americans differs by age, sex, race/ethnicity, income, and education level. J Acad Nutr Diet 2013;113(2):297\u0026ndash;306.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicklett EJ, Kadell AR. Fruit and vegetable intake among older adults: a scoping review. Maturitas 2013;75(4):305\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsnicar F, Leeming ER, Dimidi E, Mazidi M, Franks PW, Al Khatib H et al. Blue poo: impact of gut transit time on the gut microbiome using a novel marker. Gut 2021;70(9):1665\u0026ndash;1674.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis C, Bryan J, Hodgson J, Murphy K. Definition of the Mediterranean Diet; a Literature Review. 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Nutrients 2024;16(7).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4990604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4990604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Global Burden of Disease 2017 (GBD-2017) study identified high sodium intake, low whole grain intake, and low fruit consumption as key dietary risk factors for non-communicable diseases (NCDs). We hypothesize that individual characteristics and lifestyle factors influence these dietary risks, thereby modulating the composition of the gut bacterial and fungal communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2020 to 2024, we enrolled 1001 participants from four Spanish regions. Participants completed a short Food Frequency Questionnaire (sFFQ) at baseline, month six, and month 12 (n = 2475). Age, gender, geography, and seasonal factors significantly shaped dietary patterns, with older age and healthier diets, especially those rich in fruits and vegetables, linked to increased gut microbiome diversity. Participants generally consumed less legumes, whole grains, and nuts but exceeded recommended red meat and sugar intake levels, with men showing poorer dietary habits and faster gut transit times correlating with distinct microbiome profiles and lower diversity. Using machine learning techniques, dietary intake can be predicted by the gut microbiome composition. Participants can learn about the study, their diet and their microbiome here\u003c/p\u003e\n\u003cp\u003e(https://manichanh.vhir.org/POP/;username:reviewers;password:reviewers)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdherence to national dietary guidelines, particularly the Mediterranean diet, enhances gut microbial diversity. Personal, lifestyle, and geographic factors significantly influence dietary quality, highlighting the need for targeted interventions. The study suggests that improving dietary patterns positively impacts the gut microbiome and overall health in Spain.\u003c/p\u003e","manuscriptTitle":"Personal traits, lifestyle decisions, and geography shape our dietary intake and consequently our bacterial and fungal gut microbiome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 09:48:02","doi":"10.21203/rs.3.rs-4990604/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":"12c6cf44-e451-4f6c-8b6e-96d3b646a42a","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-04T16:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 09:48:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4990604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4990604","identity":"rs-4990604","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}