nutrients Article Association of the Gut Microbiota with the Host’s Health through an Analysis of Biochemical Markers, Dietary Estimation, and Microbial Composition Maite Villaseñor-Aranguren 1,† , Carles Rosés 2,† , José Ignacio Riezu-Boj 1,3 , Miguel López-Yoldi 1 , Omar Ramos-Lopez 4 , Anna M. Barceló 2 and Fermín I. Milagro 1,3,5,6, * 1 Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain 2 Servei de Genòmica, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain 3 Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain 4 Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico 5 Department of Nutrition, Food Science and Physiology, University of Navarra, 31008 Pamplona, Spain 6 Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y la Nutrición (CIBERobn), Carlos III Health Institute, 28029 Madrid, Spain * Correspondence: fmilagro@unav.es † These authors contributed equally to this work. Abstract: This study aims to analyze the relationship between gut microbiota composition and health parameters through specific biochemical markers and food consumption patterns in the Spanish population. This research includes 60 Spanish adults aged 47.3 ± 11.2 years old. Biochemical and an- thropometric measurements, and a self-referred dietary survey (food frequency questionnaire), were analyzed and compared with the participant´s gut microbiota composition analyzed by 16s rDNA Citation: Villaseñor-Aranguren, M.; sequencing. Several bacterial strains differed significantly with the biochemical markers analyzed, Rosés, C.; Riezu-Boj, J.I.; López-Yoldi, M.; Ramos-Lopez, O.; Barceló, A.M.; suggesting an involvement in the participant´s metabolic health. Lower levels of Lactobacillaceae Milagro, F.I. Association of the Gut and Oscillospiraceae and an increase in Pasteurellaceae, Phascolarctobacterium, and Haemophilus were Microbiota with the Host’s Health observed in individuals with higher AST levels. Higher levels of the Christensenellaceae and a through an Analysis of Biochemical decrease in Peptococcaceae were associated with higher levels of HDL-c. High levels of Phascolarc- Markers, Dietary Estimation, and tobacterium and Peptococcus and low levels of Butyricicoccus were found in individuals with higher Microbial Composition. Nutrients insulin levels. This study also identified associations between bacteria and specific food groups, such 2022, 14, 4966. https://doi.org/ as an increase in lactic acid bacteria with the consumption of fermented dairy products or an increase 10.3390/nu14234966 in Verrucomicrobiaceae with the consumption of olive oil. In conclusion, this study reinforces the idea Academic Editor: Lauri Byerley that specific food groups can favorably modulate gut microbiota composition and have an impact on host´s health. Received: 11 October 2022 Accepted: 4 November 2022 Keywords: gut microbiota; dysbiosis; metabolic health; insulin resistance; diet Published: 23 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- 1. Introduction iations. The microbiome is defined as the collective genomes of the microorganisms inhabiting a specific environment [1]. The intestinal microbiota comprises trillions of microbes and is being studied over the past years due to their physiological functions and role in the host’s Copyright: © 2022 by the authors. health [2]. There is evidence of a bidirectional relationship between the gut microbiota and Licensee MDPI, Basel, Switzerland. many organs in the human body, including the brain [3]. This article is an open access article A microbial alteration is crucial to the development of pathogenesis affecting the distributed under the terms and host’s health. An alteration in the composition or function of the gut microbiota is known conditions of the Creative Commons as dysbiosis [2], which is a word that refers to a decrease in microbial diversity, a lower Attribution (CC BY) license (https:// amount of beneficial microorganisms, or an increase in potentially harmful microbes [4]. creativecommons.org/licenses/by/ Several chronic disorders such as cardiovascular diseases, type 2 diabetes, inflamma- 4.0/). tory bowel disease, and non-alcoholic fatty liver disease (NAFLD) have been identified Nutrients 2022, 14, 4966. https://doi.org/10.3390/nu14234966 https://www.mdpi.com/journal/nutrients Nutrients 2022, 14, 4966 2 of 13 to have specific dysbiotic patterns [5]. Many of the pathologies related to gut microbiota dysbiosis are associated with metabolic complications related to insulin resistance, chronic inflammation, and oxidative stress. The implication of gut microbiota in the host’s health is attributed to the extraction of calories from the diet, absorption of nutrients, fat deposition in adipose tissue, hep- atic inflammation, and the ability to provide energy and nutrients for microbial growth and proliferation [6]. The relationship between the microbial community and the host is modulated by environmental factors such as diet and lifestyle, which may trigger changes in gut microbiota composition. The manipulation of the microbiota through nutritional strategies is considered a potential alternative when treating or preventing the development of diverse metabolic disorders. Several patterns in the microbiota have been associated with specific dietary habits, food groups, and specific nutrients. For example, a high-fat diet with low dietary fiber and high sugars could disrupt the intestinal eubiosis and impair gut permeability. It could also contribute to the reduction in microbial diversity [7]. A diet rich in simple sugars has been associated with pro-inflammatory effects, which may mediate alterations in the epithelial integrity and impair gut microbiota composition, which can lead to the promotion of metabolic endotoxemia, systemic inflammation and metabolic dysregulation [8]. The consumption of specific foods and macronutrients, the modulation of the micro- biome, and the association with health and disease are closely correlated. For example, adherence to a Mediterranean dietary pattern (MD) rich in fruit, vegetables, antioxidants, and monounsaturated and polyunsaturated fatty acids has been demonstrated to favorably modulate the gut microbiome, which probably contributes to the positive health effects of the MD [9]. These effects seemed to be caused by specific food groups characteristic of this diet. In the present work we hypothesize that the diet, though specific compounds such as fibers, pre and probiotics, and phytonutrients, can modulate the gut microbiota. This will consequently affect the host’s health, which is evaluated by anthropometric and biochemical markers. The general aim of this study is to analyze the relationship between the participants’ gut microbiota composition and their health and nutrition status by analyzing serum biochemical markers and food intake. 2. Materials and Methods 2.1. Participants The individuals participating in the BIOTAGUT project were recruited at the Center for Nutrition Research of the University of Navarra, Spain. The present study included 60 Spanish adults (41 females and 19 males) aged 47.3 ± 11.2 years old. The study included participants with a body mass index (BMI) between 19.0 and 34.9 kg/m2 , without weight variations (±3 kg), changes in pharmacological treatment or consumption of stomach protectors, and gastrointestinal problems during the last three months. Subjects had to understand and be will- ing to sign the informed consent form and comply with all the procedures and requirements of the study. Primary exclusion criteria included a history of hypertension, cardiovascular disease, and diabetes mellitus, patients diagnosed with primary hyperlipidemia or current use of lipid-lowering drugs, and lactation or pregnancy. The study followed the ethical principles of the 2013 Helsinki Declaration [10]. The Ethical Committee in Research of the University of Navarra approved the study protocol (reference 2021.074). 2.2. Anthropometric and Biochemical Measurements Anthropometric measurements, including body weight (kg) and height (cm), were collected by trained nutritionists using conventional validated methods. The SECA 216 stadiometer (SECA gmnh &co. kg, Hamburg, Germany) and the TANITA SC-330 body composition monitor (Tanita Corp, Tokyo, Japan) were used. BMI was calculated as the ratio between body weight and squared height (kg/m2) and was classified following the World Health Organization (WHO) standards [11]. Nutrients 2022, 14, 4966 3 of 13 Blood samples were drawn by venipuncture after an overnight fasting period in a clinical setting. Two tubes with EDTA and two tubes without anticoagulants were collected from each volunteer. Tubes were centrifuged for 15 min at 4500 rpm, aliquoted, and stored at −80 ◦ C. Serum samples were used for analyzing glucose, total cholesterol (TC), high- density lipoprotein cholesterol (HDL-c), triglycerides, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and insulin. These markers were analyzed with a Pentra C200 clinical chemistry analyzer (HORIBA Medical, Madrid, Spain) and suitable kits provided by the company. Serum insulin was quantified using a specific enzyme-linked immunosorbent assay (Mercodia, Uppsala, Sweden) and read with an automated analyzer system (Triturus, Grifols, Barcelona, Spain). 2.3. Dietary Estimation A validated food frequency questionnaire (FFQ) that included 137 food items with corresponding portion sizes was used to estimate the habitual dietary intake [12]. Par- ticipants indicated the number of times they had consumed each food group or specific food during the previous month according to frequency categories, including daily, weekly, or monthly. Macronutrient (%) and total energy intake (kcal) were estimated with ad hoc software and the information available from valid Spanish food composition tables. Specific food groups include vegetables, fruit, legumes, cereals, whole grains, dairy intake, yogurt intake, fermented dairy products, meat, cold meat (embutidos), olive oil, soda, and light soda. A specific analysis of the fat consumption included total cholesterol, trans fat, monounsaturated (MUFA), polyunsaturated (PUFA), and saturated fat intake. 2.4. Fecal Sample Collection, DNA Extraction, and Metagenomic Data The fecal samples were self-collected by the volunteers using OMNIgene.GUT kits from DNA Genotek (Ottawa, ON, Canada), according to the instructions provided by the company. The DNA extraction from fecal samples was performed with a QIAamp® DNA kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol. Bacterial DNA sequencing was performed by the Servei de Genòmica from the Uni- versitat Autònoma de Barcelona (Bellaterra, Cerdanyola del Vallés, Spain). We analyzed the variable regions V3–V4 of the prokaryotic 16S rRNA (ribosomal Ribonucleic Acid) gene sequences, which gives 460 bp amplicons in a two-round PCR protocol. Initially, PCR is used to amplify a template out of a DNA sample using specific primers with overhang adapters attached that flank regions of interest. The full-length primer sequences were: Forward Primer: 50 TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG and Reverse Primer: 50 GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHV GGGTATCTAATCC. PCR was performed in a thermal cycler using the following conditions: 95 ◦ C for 3 min, 25 cycles of (95 ◦ C for 30 s, 55 ◦ C for 30 s, and 72 ◦ C for 30 s), and 72 ◦ C for 5 min. To verify that the specific primers had been correctly attached to the samples, 1 µL of the PCR product was checked on a Bioanalyzer DNA 1000 chip (Agilent Technologies, Santa Clara, CA, USA). The expected size on a Bioanalyzer was ≈550 bp. Following this procedure, using a limited-cycle PCR, sequencing adapters, and dual indices barcodes, Nextera® XT DNA Index Kit, FC-131-1002 (Illumina, San Diego, CA, USA), were added to the amplicon, which allows up to 96 libraries for sequencing on the MiSeq sequencer with the MiSeq® Reagent Kit v3 (600 cycles) MS-102-3003 to be pooled together. The libraries were quantified using a fluorometric method and dilution of the samples before pooling all samples. Finally, paired-end sequencing was performed on a MiSeq platform (Illumina) with a 600 cycles Miseq run, a 20 pM sample, and a minimum of 20% PhiX. The mean reads obtained was 164,387. Samples with more than 40,000 reads were used for further analysis. The authors deposited all the sequencing data in SRA (Sequence Read Archive), and the accession key has been included in the text (PRJNA623853). The 16S rRNA gene sequences obtained were filtered following the quality criteria of the OTUs (operational taxonomic units) processing pipeline LotuS (release 1.58). This Nutrients 2022, 14, 4966 4 of 13 pipeline includes UPARSE (Highly accurate OTU sequences from microbial amplicon reads), de novo sequence clustering, and removal of chimeric sequences and phix contaminants for identifying OTUs and their abundance matrix generation. Taxonomy was assigned using HITdb (Highly scalable Relational Database). OTUs with a similarity of 97% or more were referred to as species. The abundance matrix of species, genera, families, class, order and phyla was normalized using the centered log-ratio (CLR) transformation using the R packages “compositions” and “zCompositions” [13]. 2.5. Statistical Analysis Microbiome Analyst [14,15] was used to analyze the participants’ microbiome compo- sition comparing the different anthropometric measures, biochemical markers, and dietary estimation. For each variable, the population was divided into two groups (high and low levels) according to the median. To analyze the statistical differences in microbiota profiles between groups, a Zero-inflated Gaussian approach of Metagenome-Seq using the cumula- tive sum scaling (CSS) normalization and Student’s t-test through a CLR normalization, followed by FDR correction, were performed. 3. Results 3.1. Characteristics of the Study Population Characteristics of the population that participated in this study, including age, an- thropometric measures, and biochemical data, are shown in Table 1. Data for the whole population, and for the high and low groups according to the median, are shown. Table 1. Characteristics of the participants. All Participants High Group Low Group Variables (n = 60) (n = 30) (n = 30) Age (y) 47.3 ± 11.2 55.8 ± 5.8 38.3 ± 9.3 Weight (kg) 69.9 ± 14.1 79.8 ± 11.6 59.0 ± 5.0 BMI (kg/cm2 ) 24.6 ± 3.9 27.5 ± 2.8 21.4 ± 1.6 Glucose (mg/dL) 94.5 ± 11.3 101.3 ± 11.1 86.3 ± 3.9 Total cholesterol (mg/dL) 219.4 ± 35.4 247.5 ± 21.7 192.8 ± 21.0 HDL (mg/dL) 64.3 ± 15.6 78.0 ± 11.9 52.7 ± 6.8 Triglycerides (mg/dL) 79.0 ± 36.6 102.2 ± 36.1 53.1 ± 9.0 Insulin (µIU/mL) 8.4 ± 4.3 11.4 ± 3.7 5.2 ± 1.6 AST (µ/L) 22.0 ± 14.3 27.3 ± 17.7 16.2 ± 2.4 ALT (µ/L) 22.0 ± 20.6 30.2 ± 25.1 12.8 ± 2.4 Data are expressed as mean ± standard deviation. 3.2. Dietary Intake Characteristics A FFQ evaluated the dietary intake characteristics of the population participating in this study. Table 2 shows the main characteristics of the dietary intake from the self-reflected questionnaire. Data for the whole population, and for the high and low groups according to the median, are shown. Table 2. Nutritional characteristics of the population. High Group Low Group High Group Low Group Variables All (n = 60) Variables All (n = 60) (n = 30) (n = 30) (n = 30) (n = 30) Energy intake 2381 ± 796 3118 ± 801 1819 ± 299 Yogurt (g/day) 75.4 ± 78.2 129.9 ± 72.4 18.0 ± 21.8 (kcal/day) Carbohydrate Fermented 38.2 ± 7.7 43.8 ± 4.2 31.9 ± 5.3 93.0 ± 79.8 149.6 ± 72.2 34.3 ± 22.7 intake (%) dairy (g/day) Protein intake (%) 18.3 ± 3.4 21.1 ± 3.0 15.9 ± 2.0 Meat (g/day) 169.4 ± 75.6 263.8 ± 166.0 115.1 ± 34.6 Fat intake (%) 41.3 ± 6.8 46.8 ± 5.1 36.3 ± 3.2 Cold meat (g/day) 7.5 ± 9.2 12.8 ± 10.1 1.7 ± 1.7 Fiber intake (g/day) 28.9 ± 11.8 39.9 ± 9.6 20.1 ± 4.7 Olive oil (g/day) 27.6 ± 27.2 45.8 ± 30.5 12.6 ± 5.6 Vegetables (g/day) 432.6 ± 206.8 632.5 ± 194.1 281.3 ± 90.6 Soda (g/day) 13.2 ± 29.5 26.5 ± 36.5 0.0 ± 0.0 Nutrients 2022, 14, 4966 5 of 13 Table 2. Cont. High Group Low Group High Group Low Group Variables All (n = 60) Variables All (n = 60) (n = 30) (n = 30) (n = 30) (n = 30) Fruit (g/day) 301.8 ± 204.7 527.6 ± 343.2 156.6 ± 65.3 Soda light (g/day) 20.3 ± 42.2 45.9 ± 59.2 0.0 ± 0.0 Total cholesterol Legumes (g/day) 23.8 ± 12.6 32.2 ± 12.0 14.9 ± 4.7 526.5 ± 210.7 727.4 ± 237.0 379.5 ± 88.0 (mg/day) Cereals (g/day) 166.3 ± 98.5 241.0 ± 83.4 97.1 ± 36.9 Trans fat (g/day) 0.8 ± 0.5 1.1 ± 0.4 0.4 ± 0.1 Monounsaturated Whole grains (g/day) 35.8 ± 39.9 64.8 ± 36.8 5.8 ± 8.5 48.8 ± 22.0 69.2 ± 22.7 33.4 ± 7.6 fat (g/day) Polyunsaturated Dairy intake (g/day) 301.3 ± 187.5 442.3 ± 142.3 154.8 ± 80.1 16.8 ± 6.5 24.3 ± 7.6 11.6 ± 2.7 fat (g/day) Saturated fat (g/day) 30.4 ± 11.9 41.3 ± 8.7 21.4 ± 5.8 Data are expressed as mean ± standard deviation. 3.3. Microbiota Composition: Biochemical Markers The population was divided into two groups (low and high levels) according to the me- dian of the circulating levels of the biochemical parameter, as shown in Table 1. Significant relations (FDR < 0.05) were observed between some of the biochemical markers analyzed and specific families or genera (Figure 1). No statistical differences were found at the levels of phylum, class, order and species. The biochemical markers that were significantly related to changes in the gut microbiota composition were AST, HDL cholesterol, and insulin levels. The following data are expressed as box plots in Figure 1. Several beneficial bacteria, such as Oscillospiraceae, Lactobacillaceae, Rikenellaceae, and Porphyromonadaceae, were less abundant in the participants with higher AST levels. However, Pasteurellaceae, Phascolarctobacterium, and Haemophilus were more abundant in the same group. Higher levels of the Christensenellaceae and lower levels of Peptococcaceae were found in the individuals with higher HDL-cholesterol. In addition, higher levels of Phasco- larctobacterium and Peptococcus and a lower abundance of Butyricicoccus was detected in individuals with higher insulinemia. The whole summary of these results at Family and Genus levels are presented as Supplementary Material in Figure S1. The different diversity and richness indexes (i.e., Shannon, Chao-1 or Simpson) were not associated with any of the biochemical markers in the studied population. Nutrients 2022, 14, x FOR PEER REVIEW 6 of 14 Nutrients 2022, 14, 4966 6 of 13 AST levels (u/L) Lactobacillaceae, FDR=0.007 Pasteurellaceae; FDR=0.018 Oscillospiraceae; FDR=0.001 Porphyromonadaceae; FDR=0.011 Rikenellaceae; FDR=0.011 Haemophilus; FDR=0.013 Lactobacillus; FDR=0.013 Phascolarctobacterium; FDR=0.001 HDL-cholesterol (mg/dL) Peptococcaceae; FDR=0.019 Christensenellaceae; FDR=0.011 Insulin levels (µIU/mL) Butyricicoccus; FDR=0.011 Phascolarctobacterium; FDR=0.003 Peptococcus; FDR=0.022 Figure 1. Bacterial taxa differing significantly in abundance when comparing the groups of individ- uals with high and low levels of biochemical biomarkers (FDR < 0.05): AST, HDL-cholesterol and insulin. Red boxes represent participants with lower levels, and blue boxes represent participants with higher levels of the specified biochemical marker. Nutrients 2022, 14, 4966 7 of 13 3.4. Microbiota Composition: Nutritional Markers As shown in Table 2, the population was divided into two groups (low and high intake levels) according to the median of the intake of the different nutritional data. The bacteria whose levels were significantly different (FDR < 0.05) when comparing both groups, high and low intake of each parameter, are shown in Figures 2–4. These figures represent the interface between the dietary factors presented in Table 2 and the gut bacterial composition Nutrients 2022, 14, x FOR PEER REVIEW 8 of 14 at the different taxonomic levels. No statistical differences were found at the levels of phylum, class, order and species. Figure 2. Bacterial Figure 2. Bacterial taxa differing differing significantly significantlyin inrelation relationto tototal totalenergy, energy,carbohydrate, carbohydrate,protein, protein,and and fiberintake fiber intake (A), (A), as as well as different types of of fat: fat: monounsaturated, monounsaturated,polyunsaturated, polyunsaturated,saturated, saturated,and and trans (B) (FDR < 0.05). Red boxes represent participants with lower consumption, and trans (B) (FDR < 0.05). Red boxes represent participants with lower consumption, and blue boxes blue boxes representparticipants represent participantswith withhigher higherconsumption consumptionofofthethespecified specified food food group group compared compared to to thethe me- median. dian. Total energy, carbohydrate, and protein intake were associated with higher abundance of Total energy, Paraprevotella, carbohydrate, and Enterobacteriaceae, andUnclassified protein intake were associated Clostridium, with Lower respectively. higherlevels abun-of dance of Paraprevotella, Enterobacteriaceae, and Unclassified Clostridium, respectively. Melainabacteriaceae were found in individuals with higher fiber intake. There were significant Lower levels differences of Melainabacteriaceae when were abundance comparing the bacterial found in individuals with higher of the individuals withfiber intake. higher and There were significant differences when comparing the bacterial abundance of lower consumptions of different types of fats, including monounsaturated, polyunsaturated, the indi- viduals with saturated, andhigher trans. and lower consumptions For example, higher levelsofof different types of fats, including Phascolarctobacterium mono- and Butyricicoccus unsaturated, polyunsaturated, saturated, and trans. For example, higher were found in the groups with higher intake of monounsaturated and polyunsaturated levels of Phasco- larctobacterium fats, respectively.and Butyricicoccus Higher were found consumption in the fat of saturated groups with higherwith was associated intake of mono-in an increase unsaturated and polyunsaturated Acidaminococcaceae fats, respectively. and Phascolarctobacterium, Higher in and a decrease consumption of saturated Erysipelaclostridium. fat A higher was associated with an increase in Acidaminococcaceae and Phascolarctobacterium, intake of trans fat was related with higher levels of Phascolarctobacterium. and a decrease in Erysipelaclostridium. A higher intake of trans fat was related with higher levels of Phascolarctobacterium. Nutrients 2022, 14, x FOR PEER REVIEW 9 of 14 Nutrients 2022, 14, 4966 8 of 13 Figure 3.3. Bacterial Figure Bacterialtaxa taxadiffering differingsignificantly significantly when when comparing comparing individuals individuals withwith higher higher and and lower lower con- consumption of yogurt and fermented dairy (A), fruit and whole grains (B), and olive oil (C) sumption of yogurt and fermented dairy (A), fruit and whole grains (B), and olive oil (C) (FDR < 0.05). (FDR < 0.05). Red Redrepresent boxes boxes represent participants participants withconsumption, with lower lower consumption, andboxes and blue blue boxes represent represent partic- participants ipants with higher consumption of the specified food group. with higher consumption of the specified food group. The results The results in in Figure Figure 33 show show higher higher levels levels of of Enterobacteriaceae, Eubacteriaceae, and Enterobacteriaceae, Eubacteriaceae, and Streptoccaceae were observed in individuals with higher fruit consumption. Streptoccaceae were observed in individuals with higher fruit consumption. Lower levels Lower levels of Melainabacteriaceae of Melainabacteriaceaewere werefound foundininthe thegroup groupofof individuals individuals with with higher higher consumption consumption of of whole grains. The results also show higher levels of the Lactobacillaceae family whole grains. The results also show higher levels of the Lactobacillaceae family in individuals in indi- viduals with withconsumption higher higher consumption of yogurt ofandyogurt and fermented fermented dairyAproducts. dairy products. A high con- high consumption of sumption olive ofrelated oil was olive oil was related to lower amountstooflower amounts Desulfovibrio butofhigher Desulfovibrio levels of but higher levels of Phascolarctobacterium Phascolarctobacterium and Verrucomicrobiaceae and(very Verrucomicrobiaceae close taxonomically(very close to the taxonomically genus to the genus Ak- Akkermansia). kermansia). Individuals with higher consumption of light soda had lower abundance of the families Clostridiaceae, Methanobacteriaceae, and Dehalobacteriaceae and lower levels of the genera Ruminococcus and Eggerthella. A higher consumption of meat was related with higher levels Nutrients 2022, 14, 4966 9 of 13 Nutrients 2022, 14, x FOR PEER REVIEW 10 of 14 of Phascolarctobacterium but a decrease in Oscillospira. Butyricicoccus, a butyrate-producing genus, was less abundant in individuals with lower consumption of cold meat (embutidos). Figure 4. Figure 4. Bacterial Bacterial taxa taxa differing differing significantly significantly in in relation relationto tothe theconsumption consumptionofoflight lightsoda soda(A) (A)and and meat and cold meat intake (embutidos) (B) (FDR < 0.05). Red boxes represent participants with lower meat and cold meat intake (embutidos) (B) (FDR < 0.05). Red boxes represent participants with lower consumption, and blue boxes represent participants with higher consumption of the specified food consumption, group comparedandtoblue the boxes median.represent participants with higher consumption of the specified food group compared to the median. Individuals with higher consumption of light soda had lower abundance of the fam- The whole summary of the relationship between nutritional markers and microbiota ilies Clostridiaceae, Methanobacteriaceae, and Dehalobacteriaceae and lower levels of the composition at Family and Genus levels are presented as Supplementary Material in genera Ruminococcus and Eggerthella. A higher consumption of meat was related with Figures S2 and S3, respectively. In the studied population, the different alpha-diversity higher levels of Phascolarctobacterium but a decrease in Oscillospira. Butyricicoccus, a butyr- indexes were not associated with any of the nutritional markers evaluated. ate-producing genus, was less abundant in individuals with lower consumption of cold meat 4. (embutidos). Discussion The whole summary of the relationship between nutritional markers and microbiota 4.1. Analysis of Microbiota with Metabolic and Hepatic Health composition at Family and Genus levels are presented as Supplementary Material in Fig- ures In S2the analysis and between the S3, respectively. Ingut themicrobiota and healththe studied population, status, we identified different biochemical alpha-diversity in- markers of metabolic and hepatic health that presented strong associations dexes were not associated with any of the nutritional markers evaluated. with specific bacteria in the microbiome. Metabolic pathologies related to obesity, insulin resistance, inflammatory 4. Discussionconditions and metabolic endotoxemia are highly associated with dietary and lifestyle factors, which have a crucial role in the modulation of the microbiome [16]. Some 4.1. Analysis of Microbiota with Metabolic and Hepatic Health Nutrients 2022, 14, 4966 10 of 13 specific bacteria have been related to metabolic benefits, and others have been related to a worse metabolic state [17]. Some metabolic diseases, particularly those related to insulin resistance and low-grade inflammation, have been associated with lower microbial diversity and dysbiosis. On the contrary, metabolic health and leanness have been associated with higher gut microbial diversity and richness [17]. Gut microbiota dysbiosis has been also implicated in the pathogenesis of liver diseases such as alcoholic and non-alcoholic fatty liver disease [18]. Concerning hepatic health, a decrease in Oscillospiraceae and Lactobacillaceae and an increase in Pasteurellaceae, Phascolarctobacterium and Haemophilus has been described in individuals with altered liver function through the analysis of AST and ALT [16–22]. Elevated levels of AST have been associated with hepatic dysfunction and higher levels of inflammation [19]. In our results, higher AST levels were related to lower abundance of Oscillospira and Lactobacillus, which are two genera that have been previously associated with health benefits in humans. Different studies have described that metabolic pathologies are usually accompanied by gut barrier dysfunction, which may cause increased gut permeability, translocation of bacteria, and a pro-inflammatory state in the body. This state of gut dysbiosis can contribute to an increased absorption of lipopolysaccharides and metabolic endotoxemia, which has also been related to an increased risk of insulin resistance [6,17]. Our results show that some SCFA-producing bacteria, such as Butyricicoccus, are more abundant in participants with lower insulin resistance. This suggests that butyrate might benefit insulin metabolism. In our results, there was also an increase in the Phascolarctobac- terium genus in individuals with higher levels of AST, as well as a significant correlation between Phascolarctobacterium, insulin levels and trans-fat intake. Previous evidence stated that an increase in the Phascolarctobacterium was associated with a decrease in bacterial diversity in patients with NAFLD, type 2 diabetes, and hepatitis B infection [23]. Critical cardiometabolic parameters and dyslipidemia (i.e., low HDL-c) have been negatively associated with higher BMI and abdominal obesity. These parameters can be modulated by dietary and lifestyle factors that contribute to changes in gut microbiota composition [7,24]. In our results, individuals with higher levels of HDL-c presented a higher abundance of Christensenellaceae, suggesting a possible relation between Chris- tensenellaceae and lower cardiometabolic risk [25]. On the other hand, some bacteria may contribute to impair the cardiometabolic state. For example, low levels of Peptococcaceae have been associated with higher levels of HDL-c, whereas higher levels of Peptococcaceae have been positively correlated with insulin re- sistance [26]. Insulin resistance is associated with worse metabolic health and several car- diometabolic risk factors, such as low HDL-c [27]. Therefore, lower levels of Peptococcaceae and higher HDL-c seem to be associated with better metabolic health [28]. In our study, we have observed that Peptococcaceae was negatively associated with HDL-cholesterol. 4.2. Analysis of Microbiota and Dietary Intake Diet and lifestyle are the most important modulators of gut microbiota composition. There are complex interactions between gut microbiota, dietary factors and the genetic background that are crucial for the development of metabolic syndrome features [29]. In this research, the consumption of specific food groups, such as fermented dairy products, meat, fat, and olive oil, was significantly associated with specific bacteria in the gut microbiome. For example, we found that a high caloric intake was associated with an increase in Paraprevotella genus and Enterobacteria. These results contribute to previous evidence stating that an elevated consumption of simple sugars and carbohydrates is associated with a worse metabolic state due to the impairment in epithelial integrity and increased inflammation [30]. The association between the consumption of fermented dairy products and an increase in Lactobacilli has been previously reported. Different strains from this bacterial group are used as probiotics and contribute to the prevention of obesity and other metabolic patholo- Nutrients 2022, 14, 4966 11 of 13 gies [26,31]. In this context, we have also observed that there were higher Lactobacillus levels in the individuals with higher consumption of fermented dairy products and yogurt. High consumption of meat has been associated with an increase in inflammatory markers and adiposity [32]. Our results show a decrease in the levels of Oscillospira, a putative beneficial genus, in the individuals with higher meat consumption, suggesting a negative correlation between Oscillospira and meat intake. Extra virgin olive oil has been extensively associated with anti-inflammatory ef- fects [33]. Our results show that olive oil consumption is associated with an increase in the abundance of Verrucomicrobiaceae, which is very close taxonomically to the genus Akkermansia. Akkermansia muciniphila has been negatively associated with overweight, obesity, hypertension, and type 2 diabetes [34]. On the contrary, a high-fat diet rich in saturated and trans fats has been associated with a pro-inflammatory state characterized by a reduction in microbial diversity, increased intestinal permeability, and lipopolysaccharide translocation [35]. In our study, individuals with higher consumption of saturated and trans fat had higher amounts of Phascolarcto- bacterium, which is a lipolytic genus that secretes extracellular esterase to break down triglycerides and its hydrolyzates [36]. 5. Conclusions This study suggests associations between gut microbiota composition, hepatic health, and insulin resistance status, where the consumption of specific foods related with inflam- mation features plays an important modulatory role. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/nu14234966/s1, Figure S1: Biochemical markers and microbiota composition: Family and Genus, Figure S2: Nutritional markers and microbiota composition: Family, Figure S3: Nutritional markers and microbiota composition: Genus. Author Contributions: Conceptualization and methodology: F.I.M., A.M.B., M.L.-Y. and J.I.R.-B.; methodology: M.V.-A. and C.R.; writing—original draft preparation: M.V.-A., C.R., O.R.-L. and F.I.M.; writing—review and editing: F.I.M., J.I.R.-B. and M.V.-A.; project administration: A.M.B. and F.I.M.; funding acquisition: A.M.B. and F.I.M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Centro Tecnológico para el Desarrollo Industrial (CDTI) through the program Consorcio de Investigación Empresarial Nacional (Programa CIEN, BIOFOOD Project) and by CIBERobn (Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CB12/03/30002). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University of Navarra. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions. Acknowledgments: The authors express their gratitude to all of the participants involved in this study, for their dedication and contribution to the research. 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