1. Introduction

InOver the past two decades, advances in high-throughput DNA sequencing have propelled reresearch into the human microbiome and its link to metabolic health. Numerous s has been driven by advances in high-throughput DNA sequencing. Studies emphasize howhave revealed associations between changes in the gut microbiota composition correlate with and metabolic disorders likesuch as obesity [1^[1][2],2], Type 2 Diabetes Mellitus (T2DM) [3^[3],4], and cardiovascular diseases [5,6]^[4][5]. These shiInfts in the complex microbiome ecosystem result from various factors,luencing factors includinge age [7]^[6], gender [8]^[7], geographicaly origin [9]^[8], genetics [10]^[9], and environmental infeluences, such as gastrointestinal transit time [11–13] and medication useements [14]^[10][11][12], with dietary choices ^[13] plassumying a particularly influential role [15]significant role.

To enhance our understanding of the complex interomprehend the intricate relationship between dietary patterns, the gut microbiome, and their impaeffect on host metabolism and health, researchers have introduced the concept of ententerotypes as a valuable classification framework [16,17]^[14]. EntTherotypesse categorize individuals based on the compositional attributes of their gut microbiota. However, understanding composition. To unravel the functional implications of these eenterotypes on host metabolism requires the, it is necessary to integration of e metabolomics, a systematic discipline concerned with the study of that studies small molecular compounds or metabolites.

. Metabolomics fuprnishovides a comprehensive overview of the spectrum of metabolites produced by the gut microbiota in response to diet [15,18,19].

In tThis review, literature research focused on the keywords “diet”, “microbiome analysis”, “metabolomics”, “enterotypes”, “Prevotella”, ans on method “Bacteroides” in the PubMed database. We examined the literature from 2011 to the present to align with the first paper on enterotypes. It is important to note that the methodological section, exploring micranalyzing the microbiome research and connections between diet and microbial, particularly metabolites, considers earlier publications to provide a holistic understanding of the topic.

We deomics, and explve into microbiome analysis methods, with a specific emphasis on metabolomics. Additionally, we investigate the impact of dietares how dietary choices on the production of influence microbial metabolites and offer. It provides an overview of studies examining the connectionon the link between enterotypes and diet. Finally, we e The review concludes by evaluateing how metabolomics can enhanceimprove personalized dietary recommendations, drawing. It draws insights from the intricatecomplex relationship between enterotypes and diet.

2. Metabolomic Approaches to Decode Diet–hods in Microbiome Relationshipssearch: An Overview

Tohe understand complex relationships between human nutrition and the composition of the Human Microbiome Project initiative (HMP) has been instrumental in establishing fundamental frameworks for microbiomta analysis ^[15]. Specifically, amplicon-based sequencing of 16S ribosomal ribonucleic acid (rRNA) has been a widely used approach for to analyzinge microbiome composition.

Key Sfignificant contributions in this area have been made by the Human Microbiome Projectures, such as those involved in the HMP initiative, which played a crucial role in establishing fundamental frameworks for a microbiota analysis [20]have made significant contributions. The shift from amplicon-based sequencing involves amplifying hypervariable regions within the 16S gene, unique to specific genera, followed by sequencing. However, it is limited to sequencing bacteria and archaea and provides restricted taxonomic resolution [21]. As this technique only allows conclusions about the presence and relative abundances of particular bacterial genera, it does not reveal insight into their functional potential [22].

Ato metagenomic analyses represents a milestone, as demonstrated by influential projects the cost of sequencing has decreased, there has been a notable shift towards metagenomic analyses of microbes, exemplified by the impactful uch as MetaHIT project [23]^[16]. ThMe metagenomic approachmethods not only characterizes the treveal taxonomic composition of a sample but also reveals itsoffer insights into functional metabolic capabilities. For instance, it enables the, such as identification of ying genes encodingthat encode enzymes responsible for the degradation of specific breaking down food components [24]. How

Mevetapr, metagenoteomics alone cannot provide information about in microbial gene expression activity. To address this limitation, ome researchers increasingly employ large-scale metagenomics, metaproteomics, and metatranscriptomics to gain deeper insights into how microbi was introduced in 2008 by Verberkmoes et al communit^[17]. Thies respond to changing environmental conditions over time. Metaproteomics etechnique enables a detailed investigation of microbial proteins and provides insights into expressed genes and microbial functions. In 2008, Verberkmoes et al. first employed this method, which analyzes the entire set of proteins (proteome) encoded by the microbes in a sample. High-resolution mass spectrometry combined with liquid chromatography enabled the separation and identification of peptide mixtures. By connecting peptide sequences to genomic databases, specific proteins can be linked to the microorlinks them to specific microorganisms responsible for producing them [25]. Although only a portion of the protein material is currently reliably identified, ongoing efforts to standardize and catalog these data enhance the utility of metaproteomics in studies exploring the meta-omics landscape [26].

As pars. Furthermore, Booijink et al. made a significant of the progression in advanced microbiome analysis techniques, Booijink et al. conducted the initialment by initiating a metatranscriptome analysis of the human fecal microbiome [27]. Metatranscriptomics, which provides tractable links between the genetic potential of a microbe and its molecular activity [28]. Metatranscriptomic methods involve sequencing RNA actively transcribed by a microbial community, presenting more complex technical challenges than metagenomics, including the maintenance of RNA integrity and the selective isolation of messenger RNA. Despite these obstacles, the significance of metatranscriptomicsial genetic potential and molecular activity in microbiome research continues to grow [29]^[18].

Thise seamless progression from amplicon-based sequencing through metagenomics to metaproteomics and metatranscriptomics has established a comprehensive foundation for exploring the microbiome’s functional aspects. However, t of the microbiome. To gain a truly holistic understanding of its role in human health, we delvethe focus shifts further into the realm of metabolomics.

Metabolomic techniques provideffer insight into the overall metabolic status and interactions occurring within the microbiome. A pivotal component of the interaction of the gut microbiota with the host physiology is the variety of small molecules (metabolites) produced by the microbial community and the host. Due to the dDespite the diverse characteristics of metabolites in terms of size, polarity, and abundance, identifying and quantifying the complete pool of metabolites remains a challenge. M, metabolomics employs various technologies to measure defined sets of known metabolites (targeted metabolomics) or to perform a comprehensive analysis of the metabolome (untargeted metabolomics).

Tar, including mass spectrometry (MS) and nuclear magneted metabolomics offers higher sensitivity and more precise quantification by using internal standards and normalization techniques. Untargeted metabolomics detects a wider range of metabolites, including previously unknown ones, which can lead to novel hypotheses about complex meic resonance (NMR) spectroscopy, to measure defined sets of known metabolic pathways. Combining both untargeted and targeted metabolomics approaches can providetes or perform a comprehensive view of the metabolome. While handling the vast amounts of data generated by these techniques is challenging, analysis of the metabolomics offerse a direct and informative way to under^[19].

Astand the physiological stateart of the host and the interface between the host and its microbiome [30]. Metabolite profiling is increasingly used in research to understand the effects of specific gut microbial changes and the impacts of microbiome-derived or host–microbiota co-substrates on human health. There are several methods to measure microbial metabolites in stool, saliva, urine, blood, or food [19,30].

Within this array of methods, mass sis progression, the use of mass spectrometry (MS), became more popular in scientific studies due to its ability to detect metabolites with high sensitivity, impartiality, and efficiency [31]. To accurately identify and quantify metabolites in complex biological samples, MS is often preceded by cparticularly gas chromatographic separation to improve sample resolution. Gas chromatography–y-mass spectrometry (GC-MS) is particularly suitable for volatile metabolites like short-chain fatty acids (SCFAs) but can also handle non-volatile compounds like sugar metabolites, amino acids, and their derivatives when coupled with specific chemical derivatization steps. On the other hand, liand liquid chromatography–-mass spectrometry (LC-MS) is widely used for both non-polar (e.g., bile acids and lipids) and polar (e.g., purines, amino acids, vitamins) metabolite analyses. LC-MS operates at lower temperatures and uses gentler ionization methods than GC-MS, making it suitable for larger, non-volatile, and less stable metabolites [19].

Nu, has beclear magnetic resonance (NMR) spectroscopy is another method used in metabolomics, albeit with lower sensitivity compared to mass spectrometry. NMR allows the quantification of abundant metabolites with relatively simple sample preparation and provides structural information, which is valuable for identifying new microbiota-related compounds. However, identifying the structure of spectral hits can be challenging due to the diversity of microbial products, many of which are not well characterized. Both MS and NMR spectroscopy enable an untargeted metabolite analysis and can be used to trace isotopes to investigate nutrient assimilation and metabolic activity in the microbiota. Despite these challenges in characterizing specific metabolic flux within the microbiota using isotopic labeling—a hurdle arising from the shared presence of many metabolites among the host and various microorganisms in the microenvironment [19]—microbiome research has predominantly centered on analyzing me more prevalent in scientific studies. MS has the ability to detect metabolites in serum and feces. However, recent literature highlights the significance of also examining the effects of metabolites found in urine [32] and saliva [33] on overall health. Limiting the assessment to a single biofluid may provide researchers with a narrow and potentially misleading perspective. A more comprehensive understanding can be achieved by considering multiple biofluids in these studies [18].

Given with high sensitivity, impartialithe complexity and myriad individual factors that influence the interactions between the diet, microbiota,, and efficiency and^[20]. host, it is critical to bring all thMese data together to provide the best possible insights and, in the best case, integrate them into public health policy and dietary recommendations. Combining metabolomicstabolomics, when combined with artificial intelligence (AI) and machine learning (ML) techniques will revolutioniz, has the potential to enhance our understanding of the microbiome and its import's significance for personalized medicine, biomarker discovery, drug development, and nutrition. AI and ML algorithms can help researchers integrate data from various sources, such as genomics, metagenomics, and metabolomics, to provide a holistical recommendations unders^[21].

Etandhing of microbial communities. This can aid in identifying metabolic pathways, interactions, and functions within the microbiome [34]. Mecal considerations in metabolomics datasets are often characterized by many variables, making a data analysis and interpretation challenging. Fortunately, AI techniques offer valuable assistance in this domain by efficiently identifying the most relevant metabolites for specific research questions. By leveraging AI, researchers can streamline feature selection, enhancing their ability to extract meaningful insights from complex metabolomics data. ML models can be harnessed to construct predictive models that establish connections between microbiome composition, metabolite profiles, and various health outcomes or disease states. These models, driven by AI, illuminate the potential roles of the microbiome in numerous physiological processes, shedding light on previously obscured links between microbial communities and human health [35].

Iresearch are of utmost importance, emphasizing the n the realm of metabolomics research, ethical considerations are paramount. Researchers must uphold fundamental principles to ensure the responsible conduct of both metabolomics methods and AI/ML applications. Ped for privacy and , informed consent are foundational. In a metabolomics analysis, researchers must ensure participant privacy and obtain informed consent when collecting biological samples. P, and transparency in handling participants must understand how their biological samples will be used and the potential implications of the research. In an AI-driven metabolomics analysis, particularly in personalized medicine, individuals should be informed about how AI algorithms will use their data to shape healthcare decisions. Obtaining informed consent is crucial for upholding individuals’ rights and autonomy [36]. Industry influence is an overarching ethical issue across these fields. Disclosure of financial ties and potential conflicts of interest is critical to maintaining transparency and trust in research a data. Collaboration among researchers, ethicists, policymakers, and healthcare. Adequate regulatory oversight is necessary to ensure that industry-driven research and products meet rigorous standards and prioritize public health over profit. To effectively professionals is essential to address these ethical concerns, it is crucial to foster collaboration among researchers, ethicists, policy makers, and healthcare professionals. Together, they can develop guidelines, regulationsresponsible advancement, and best practices that promotmaximize the responsible and equitable advancement benefits of metabolomics, microbial research, and personalized nutrition, maximizing their benefits f for society while minimizing potential risks [37]^[22][23].

3. The Tight Interaction between Diet, the Gut Microbiome, and Its Metabolites

NutriThents significantly impact microorganisms in the gut, either aiding or hindering their growth. Moreover, certain gut investigation of the human microbes extract energy from specific dietary elements, giving them a competitive edge. This complex interactioniome has experienced significant expansion, with a specific focus on the intricate relationship between diet and , the gut microbiome also shapes the production of diverse, and microbial metabolites, subsequently affecting our overall health.

3.1. Carbohydrates and Dietary Fiber

Indigestible carboThydrates, obtained from various dietary—mainly plant-based—sources, play a key role in this process [38,39]. These sis interplay substances, also known as microbiota-accessible carbohydrates (MACs) were defined by Sonnenburg et al. [40]. They include dietary fibers such as resistant starch, inulin-type fructans, fructooligosaccharides, or pectin. They reach the colon without being digested because the human body lacks the necessary enzymes for their degradation. In contrast to humans, thetially influences our overall health, as nutrients impact microbiome possesses an array of glycoside hydrolases and polysaccharide lyases, collectively referred to as carbohydrate-active enzymes (CAZymes) [15]. In the degradation of complex dietary carbohydrates, SCFAs are al growth, leading to the primary end products, along with the production of CO₂oduction of and H₂. SCFAs are arguably the most extensively rstudied microbial me metabolites, with numerous effects on human metabolism [41,42].

Carbohydrates and Dietary Fiber

The functions of SCFAs in maintaining host well-being and influencing health conditions are extensive. SCFAs govern various physiological and biochemical processes within the body. These include upholding the integrity of the innate gut barrier at the colonic epithelium and mucus levels [43], regulating gut motility [41], and controlling the secretion of itilization of carbohydrates and dietary fiber holds particular important gut hormones like Peptide YY (PYY) [44], serotonin [45], gastric inhibitory peptide [46], and glucagon-like peptide 1 (GLP-1) [47]. Moreover, SCFAs are involved in chromatin regulation [48,49], intricate gut–brain connections [42], and immune responses [50]. To exert these metabolic effects, binding to G-protein coupled receptors 41 and 43 (GPR41/43), particularly expressed by the enteroendocrine L cells, plays a crucial role [41]. Acetate, propionate, and butyrate constitute more than 95% of the collective pool of SCFAs. They are found in the intestinal tracts of humans at a proportionate molar ratio of approximately 60:20:20. Outside the colon, this ratio changes to 180:5:1, indicating that most propionate and butyrate are used where they are produced. However, this ratio shifts in individuals consuming Western diets high in fat and low in fiber, resulting in lower peripheral acetate levels [51]. This change is significant due to acetate’s role in metabolic diseases, particularly T2DM. Acetate is synthesized by most enteric bacteria through the pyruvate-to-acetyl-CoA pathway and by bacteria such as Blautia hydrogenotrophica, Clostridium, and Streptococcus spp. through thce. Microbiota-accessible carbohydrates (MACs), primarily obtained from plant-based sources, are indigestible carbohydrates that play a pivotal role in the interaction betwee Wood–Ljungdahl pathway [52–54]. Th the health effects of acetate are debated. Soume studies link it to reduced appetite and weight loss via GPR41/43 interaction [55] and enhanced insulin sensitivity [56], while others suggest its role in promoting obesityan body and the microbiome as^[24][25]. a substrate for tThe liver [57] and adipose tissue fat [58] production. Some studies even associate acetate with cancer cell survival under hypoxic conditions [59]. The conflicting data on acetate’s effects may result from its dynamic signaling, which can vary depending on physiological conditions and metabolic phenotypes [51,60]. Propionate is produced bmicrobiome possesses carbohydrate-active enzymes (CAZymes) that break down complex dietary Bacteroides spp., Phascolarctobacterium succinatutens, Dialister spp., cand Veillonella spp. thrbough the succinate pathway and via the acrylate pathway by Megasphaera elsdenii, Coprococcus catus, Salmonella hydratespp., Roseburia inulinivorans, and Ruminococcus obeum [52–54]. Colonocytres use propionate for intestinal gluconeogenesis via the free fatty acid receptor 3 (FFAR3) signaling pathway, or is absorbed into the portal system and taken to the liver for hepatic gluconeogenesis [61]. Human studies suggest that propionate has overall anti-obesity effects, as it can increase post-prandial GLP-1 and PYY levels, reduce weight gain, intra-abdominal fat, and intrahepatocellular lipid content, and prevent insulin sensitivity issues [61]. Propionate also exhibits anti-inflammatory properties by reducing the release of interleukin-8 (IL-8) and tumor necrosis factor α (TNF-α) from neutrophils [62]. Of all the SCFAs produced in the gastrointestinal tract by fermentation, butyrate is particularly noteworthy. It has garnered extensive attention in scientific research due to its well-documented chemistry, cellular signaling mechanisms, and various clinical benefits [51]. Blting in the production of short-chain fatty acids (SCFAs) as the primary end products. SCFAs, comprising acetate, propionate, and butyrate can be converted via the condensation of two molecules of acetyl-CoA and subsequent reduction to butyryl-CoA via the classical pathway by phosphotransbutyrylase and butyrate kinase. Butyryl-CoA can also be converted to butyrate via the butyryl-CoA/acetate-CoA transferase pathway. Important butyrate-producing genera and species are Coproccocus genus, Anaerostipes spp., Eubacterium genus, Faecalibacterium prausnitzii, a, exert various effects on human metabolism, influencing gut barrier ind Roseburia spp. [52,54,63]. Butyrate serves as the primargrity fuel source for mature colon cells^[26], sgupporting colon health, and acts as a microbial metabolite with strong anti-inflammatort motility properties^[27], both locally and systemically [64]. Additionally, butyrate plays a crucial role in reormone regulating local andon systemic immunity [65]^{[28][29][30][31]}, chromaintaining mucosaltin dynamics integrity [66]^[32][33], and inhibiting cellular-level neoplastic changes [64]. Butyrate, similar to propionate, haut-brain connections the^[34], ability to renduce obesity by stimulating the release of appetite-suppressing hormon immune responses a^[35]

Proteins and Amino Acids

Ind promoting the synthesis of leptin [67].

3.2. Proteins and Amino Acids

When the absence of fermentable fibers, become scarce, gut microbes adapt by utilizing less favorable energy sources for their growth, such as aamino acids from dietary or endogenous proteins or dietary fats [68]^[36][37][38]. Although most of the pProtein absorption in humans occurs in the small intestine, about 5–10% of dietary protein is not absorbed through the ileum and enters the colon as proteins and peptides [69,70]. The extent to which the gut microbiota utilize amino acids depends primarily on substrate availability and the luminal milieu. For example, increased pH in the colon [71] and decreased availability of carbohydrates [72] are associated with increased rates of bacterial fermentation of proteins. Amonolytic bacteria, including the bacteria exhibiting proteolytic properties are genera such as Bacteroides, and Clostridium sperfringencies, Propionibacteria, Streptococci, Bacilli, acond Staphylococci [70,73]. The ptrimarybute pathway ofto amino acid fermentation in the colon involves deamination, producing SCFAs and ammonia. The liver converts ammonia into urea, which is excreted in urine. The degradation of proteins by the microbiota results in significantly lower production of , yielding SCFAs than that derived from carbohydrates [70]. Approximately 30% of the substrates are converted into short SCFAs and branand branched-chain fatty acids (BCFAs) such as isobutyrate, 2-methylbutyrate, and isovalerate, as well as intermediates such as lactate and succinate ^[38][39]. These are often used as indicators of protein fermentation. There is limited knowledge about the role of BCFAs in humans and metabolic health [41,74]. However, recent research has highlighted the importance of their precursor compounds, breakdown of branched-chain amino acids (BCAAs), in obesity, insulin resistance, and T2DM. Elevated BCAA levels in the blood are linked to insulin resistance across diverse populations and regions. They tend to co-occur with other metabolites like aromatic amino acids (phenylalanine and tyrosine) and acylcarnitines, all of which indicate an excess of BCAAs in the body through different processes [75,76].

Wh like valine, leucile the microbial degradation of the BCAAs valine, leucine, e, and isoleucine is, associated with rather negative effects, recent data suggest that tryptophan metabolites, derived from dietary sources such as meats and nuts, play a significant role in maintaining intestinal health [19]. Tryptophan is one of the nine essential amino acids humans cannot synthesize and must obtain by dietary protein sources. The small amount that does reach the colon is converted into indole, various indole derivates, serotonin, and kynurenine under the direct or indirect control of the microbiota [77]. Tryptophan metabolites exhibit diverse functions, such as antian impaired insulin resistance, highlights the intricate relationship between protein fermentation, microbial properties against various bacteria and parasites [78], modulation of the immune system through the aryl hydrocarbon receptor (AHR) [79]metabolites, and preservation of intestinal balance by promoting mucous production and goblet cell differentiation [78]. They also affect systemic hormone secretimetabolic health  ^[20][40][41].

Dietary Fat and Bile Acids

Con, with indole stimulating the release of GLP-1, which impacts insulin secretion, appetite suppression, and gastric emptying. Indole propionic acid (IPA) acts as an antioxidant by scavenging free radicals [65].

3.3. Dietary Fat and Bile Acids

In the pastrary to previous beliefs, experts underestimated the influence of dietietary fat on the gut microbiota, believing that most fat digestion and absorption occurred in the small intestine and little to no dietary fat reached the colon in healthy individuals. This belief was rooted in the understanding that bacterial populations in the digestive tract increased as one moved from the small intestine to the colon, making significant interaction between dietary fat and gut microbiota seem unlikely [80]. Recent research has challenged the idea that dietary fat does not affect the t significantly influences the composition of the gut microbiota. Gabert et al. (2011) have shown that a substantial portion (approximately 7%) of dietary fatty acids labeled with carbon-13 are excreted in the stools of healthy individuals. Interestingly, more than 86% of these excreted fatty acids are free fatty acids, indicating that digestion failure is not the cause of fat in stool. Digestive lipases can break down triglycerides into free fatty acids. These findings suggest that dietary fat significantly impacts the, subsequently impacting the host's health gut^[42]. microbiota, contradicting prior assumptions [81].

A diet rich in fat can significantly modify the composition of the gut microbiota, resulting in an overrepresentation of bacteria that express lipopolysaccharides (LPSs) [82]. This results in a pro-inflammatory condition known as metabolic endotoxemia. Maturated fats can induce metabolic endotoxemia, characterized by elevated levels of LPS in the bloodstream^[43], leads to pro-inflammatory responses in both mice and humans. This inflammatory state is mecondition mediated through toll-like receptor 4 (TLR4) and CD14 in hematopoietic cells, culminating in weight gain, increased adiposity [83], elevated inflammatory markers in white adipose tissue (WAT) macrophages [84], and insulin resistance [85]. Simultaneously, metabolic endotoxemia is associated with increased gut permeability, possibly due to reduced expression of genes encoding tight junction proteins [86–88]. Intriguingly, these adverse effects appear to be specific to saturated fat consumption. Mice fed a diet rich in lard (saturated fat) exhibit an overabundance of specific bacterial taxa, including Bacteroides, Turicibacter, and Bilophila spp., which can p, leading to outcomes like weight gain, incromote WAT inflammation, adiposity, and impaired insulin sensitivity [89]. Mice on an unsaturated-fish-oil-rich diet exhibit an expansion of Bifidobacterium, Akkermansia, and Lactobacillus ased adipospp., with no discernible metabolic impairments. The transplantation of these distinct microbial compositions into germ-free mice replicates the respective metabolic phenotypes, underscoring the pivotal role of the gut microbiota in mediating the differential effects of dietary fat types on host health [89]. SCFAs’ beneficial effects in the context of fiber intake were previously explained. Studies describing the relationship between dietary fat and SCFAs provide partly contradictory results. Fava et al. reported humans consuming a saturated-fat-richy, and insulin resistance diet with higher levels of fecal SCFAs were associated with lower fecal energy content, suggesting that dietary fat can contribute to obesity by increasing energy harvest [90]^[44]. However, Bit is essential to note that this observation lacks direct evidence linking SCFAs to weight gain. A high-fiber diet, which also raises SCFA levels, is associated with reduced weight gain in humans [91]. The relationship between SCFAs, dietary fat, and obesity is multifaceted and not completely understood. Additional research is needed to clarify the precise role of SCFAs in metabolic syndrome and their interaction with dietary fat compared to fiber intake.

To facilitate the absorption and excretion of dietary fats, bile acids (BAs) are needed for solubilization by micelle formation in the small intestine. Not only do bile acids play an essential role in the dile acids, crucial for fat digestion and absorption of dietary fats and fat-soluble vitamins, but they also interact directly with intestinal microbiota and their metabolites [92]. Bile acids are synthesized from cholesterol in the liver and subsequently modified by gut bacteria. Approximately 95% of bile acids are reabsorbed in the distal ileum and returned to the enterohepatic circulation. However, bacterial deconjugation, dihydroxylation, and dehydrogenation prevent their reabsorption into enterocytes, allowing about 5% of BAs to proceed into the colon. In the colon, these primary BAs (e.g., cholic acid and chenodeoxycholic acid) interact with the galso engage with the gut microbiota, leading to the conversion into secondary bile acids, including deoxycholic acid (DCA) and lithocholic acid (LCA) [93]^[45]. Over 50 different BAs have been characterized as products of the interaction between primary BAs, with DCA and LCA being the two most common [94]. Maintaining a baAn imbalance between primary and secondary bile acids is crucial for host health, as an imbalance can harm the organism. These factors influence the balance, including the host’s microenvironment, antibiotic exposure, diet, and microbiota composition. High-fat diets result in elevated levels of secondary bile acids in the feces and influence how the gut microbiota processes bile acids. This leads to changes in the overall in the gut microbiota can perturb the bile acid composition, affecting the activation or inhibition of the bile acid receptor called receptor farnesoid X receptor (FXR) [95]. Recent research suggests that FXR plays a central role in regulating how bile acids influence the development of intestinal tumors, integrating factors such as diet, the microbiome, and genetic predisposition in the risk of hepatocellular carcinoma and, potentially contributing to conditions such as colorectal cancer [96]^[46].

3.4. Plant- and Animal-Derived Bioactive CompoundsPlant- and Animal-Derived Bioactive Compounds

Apart from fiber, pPlants also contain a diverse group of bioactive compounds in our diet, with -derived polyphenols being one of the most significant groups, several of which are linked to various health benefits. These compounds are ubiquitous in dietary sources like fruits, vegetables, grains, tea, coffee, and wine [97,98]. Systematic classification groups polyphenols into distinct phytochemical families based on shared structural features, including phenolic acids, flavonoids, lignans, lignins, coumarins, and stilbenes. The structural complexity of polyphenols is remarkable, with over 9000 distinct flavonoids alone identified, underscoring their structural diversity [99]. Due to their structural complexity, only a limited portion, approximately 10%, of dietary polyphenols are metabolized and absorbed in the small intestine. The remaining 90% continue to the lower gastrointestinal tract, undergoing, abundant in various dietary sources, undergo significant modifications and degradation by the gut microbiota, ultimately enhaninfluencing their absorption and bioavailability [100]^[47]. Studying polyphDenols has been challenging due to the intricacies of their structures and their spite limited bioavailability. However, evidence suggests that these compounds impact the composition and function of the gut microbial community [99]. Dietary polyp, polyphenols exert their effects through various mechanisms, including anhibit anti-inflammatory, antioxidant, and antimicrobial properties, and have been associated with positive outcomes inimpacting conditions such as cardiovascular disease [101]^[48], cancer [102]^[49], metabolic disorders [103]^[50], Alzheimer’s disease [104]^[51], and inflammatory bowel disease [105]^[52]. An intriguing hypothesis is that these compounds can have measurable physiological effects despite their low bioavailability, owing to substantial modifications of the parent compounds by the gut microbiota [99].

In addition to nutrients primariditionally present in plant-based foods, nutrients predominantly sourced from animal-derived foods, such as red meat, poultry, fish, and eggs, also interact with the intestinal microbiome. The gut microbiome is vital in metabolizing nutrients lilike choline, betaine, and l-carnitine into trimethylamine (TMA). TMA is transported to the portal circulation and undergoes subsequent oxid, interact with the gut microbiome, contributing to the formation to formof trimethylamine-N-oxide (TMAO) [106]. Li et al. (2022) updated 24 meta-analyses that analyzed the association between circulating TMAO concentration and and influencing health outcomes by including 82 additional studies^[53][54].

They identified six associations, including all-cause mortality, cardiovascular disease mortality, major cardiovascular events, hypertension, T2DM, and glomerular filtration rate, which exhibited particularly strong and compelling evidence [107]. The precise mechanism by which TMAO influences this context remains somewhat unclear. In rodent models, ds overview highlights the importance of microbial metabolites in the relationship between dietary TMAO or its precursors have been shown to accelerate arteriosclerosis and platelet aggregation [108]. Conversely, mice specifically fed L-carnitine [109] and choline [110] diets to increase plasma TMAO levels were found to have reduced aortic arteriosclerosis, suggesting potential differences in downstream effects from various nutrient precursorscomponents and human health when exploring diet-microbiome interactions.

4. Interindividual Differences in Microbial Responses to Diet According to Enterotypes

GUnderstandiven the complex interplayng the intricate relationship between a host organism, its resident n individual's microbiota, and how it reacts to various and their response to dietary elements, it becomes clear that a one-size-fits-all approach to diet is not feasible. The idea of personalized medicine, which recognizes individual variations, should also be implemented when creating tailored highlights the impracticality of a universal dietary plans due to the numerous variables involved [111]. Integrating precision nutrition approaches can decrease variability in outcomes among individuals, a recurrent challenge of nutrition research. Inconsistent results can obscure the assessment of approach. Recognizing the significance of precision nutritional interventions. Grouping individuals with divergent responses results in an underestimation of the effect magnitude and introduces substantial fluctuations. Precise nutrition aids researchers in comprehending factors contributing to diverse reactions to dietary interventions, facilitating tailored studies for metabolic heterogeneity [112]. Emerging developments in personalized n becomes imperative in designing tailored dietary plans, mitigating the challenges of variability inherent in nutrition have unveiled that how an individual’s metabolism reacts to specific foods is distinctly personal and intricately linked to the composition of their gut microbiota. In this context, an efficient approach would involve caresearch. This chapter explores the concept of enterotypes, a categorizing individuals ation based on their gut microbiota composition to enhance our understanding of how different people respond to diets, thus facilitating the navigation of the intricacies of the microbiota.

I, shedding light on their application 2011s, Arumgam et al. introduced the concept of enterotypes as an approach to stratify microbiota composition. By analyzing 33 qualified samples from diverse populations, including Europeans, Americans, and Japanese people, researchers successfully classified the samples into three distinct and robust clusters, primarily based on tcurrent status, and future directions in personalized dietary recommendations.

The presence and abundance of unique genera. Enoncept of enterotype 1 featured the dominance of Bacteroides, along with covariant taxa such as Parabacteroides, Alistipes, ans was introduced Bilophila. Einterotype 2, in contrast, displayed an inverse relationship with011 by Arumgam et al., who Bacteroides, fideanturing a higher prevalence of Prevotella, along wified three disth covariant bacteria such as Desulfovibrio and, occasionally, Succinivibrio. Eninct clusterotype 3 was primarily associated with Ruminococcus and cbased o-occurring taxa, including Akkermansia and Methanobrevibacter. They extended thei micr analysis of enterotypes to two additional datasets: 85 metagenomes from Danish individuals [113] and 154 pyrosequencing-based 16S sequences from American individuals [114]. Their findings indicated that these datasets also exhibited the characteristicbiota composition across diverse populations three-cluster division^[14]. The third clSuster displayed distinct characteristics, mainly driven by related Clostridiales groupbsequent studies, including Blautia and unclassified Lachnospiraceae [16]. In a thoseparate study,by Liang et al. analyzed 181 fecal samples from adults in Taiwan, China, revealing three distinct enterotypes within the population. Two of these e, reinforced the presence of enterotypes resembled patterns associated with Bacteroides aand revealed variationd Prevotella. Howevers, pa third unique enterotype specific to the rticularly in Asian populations ^[55]. Howemverged, characterized by a prevalence of the Enterobacteriaceae f, challenges arose regamily of bacteria. Liang et al. analyzed the diversity of microbial communities using three different clustering methods and produced nine different diversity matrices. These matrices resulted in varying countsding the definition and identification of enterotypes based on evaluation criteria [115]. Additional s, prompting further investigation.

Entudies support these findings, as they also classified the microbiota into primarily two main enterotypes associatedrotypes, initially characterized by dominant genera like with Bacteroides and Prevotella, [116–118]. Prevotella hand Bacteroides are often negatively correlated to each other, suggesting a competition for nutrients in the intestinal ecosystem between these two genera [119,120]. These findings highlight the versatility of the enterotype methodology, which appears to apply to shown consistent patterns across various populations, seemingly^[56][57][58]. independent of factors such as age, gender, cuAltural background, and geographic location. When analyzing the gut microbiota composition in diverse global populations, a consistent association between Enterotype 1 and Enterotype 2 with other bacterial groups is notably absent [121]. However, regionhough regional dietary distinctions likely contribute to subgroups within the enterotypes. This concept gains support from the consistent prevalence of Bifidobacterium variations within the Bacteroides enterotypes, notably observed in Japan [122]. Following the results presented by Gu et al. [123], the augmentation of Bifidobacteria both Baconfers advantages to the Bacteroides enteerotype regarding plasma bile acids and various metabolic parameters, including fasting bloodes and glucose,Prevotella insulin, and C peptide. Crucially, neither of the enterotypes appears to enterotypes exhibit a specific vulnerability to disease in general, as both categories have shown eequal associations with variousdiverse health conditions [124,125]^[59][60]. BuNot it is plausible that subgroups within the Bacteroides type may exhibit a lower ably, the role of Bifidobacterial load, a factor proposed as a key driver in Crohn’s disease [126].

Categorizingum in the Bacteroides enterotypes, based on compositional patterns has gained significant prominence in recent years. It offers the promise of simplifying particularly in Japan, underscores the intricate landscape of the gut microbiome. Moreover, this classification opens new avenues for microbiota-based diagnostics, therapeutic interventions, disease prevention strategies, and personalizedfluence of regional dietary recommendationhabits [127]^[61].

Applications and Current Status

They are akin to densely populated areas in the complex multidimensional sparominence of microbial communities, and their relevance continues to fuel extensive discourse within the scientific community. Jeffery et al. [128] and Knights et al. [129] highlighted that the microbiome often exhibits continuous gradients of dominant microbial taxa, challenging the idea of rigidly defined enterotypes. Identifying discrete clusters in high-dimensional data is a complex task, requiring robust statistical tests. Variations in dominant genera, like Bacteroides and Ruminococcus, enterotypes lies in simplifying the complex gut microbiome landscape, offering avenues for microbioften manifest as a continuous spectrum within and between putative enterotypes. Even when distinct effects are observed, as with Prevotella, significant vaa-based diagnostics, theriation persists within these suggested clusters [129]. In response to these intricate challenges, Costeas et al. performed an extensive meta-analysis to reconcile conflicting viewpoints regarding enterotypes. Their analysis resulted in a modified concept of enterotypes, offering a more nuanced and adaptable perspective in microbiome research [127]. Koren and colleagues reported that the methodology significantly impacts the categorization of populations into enterotypes, with distance metrics and clustering score methods exerting the most substantial influence. Thepeutic interventions, disease prevention strategies, and personalized dietary recommend using at least one absolute scoring method in conjunction with 2–3 different distance metrics to validate the presence of enterotypes. Currently, there is no widely accepted consensus oations. However, challenges in precisely defining an enterotype. Researchers working with the concept may reach opposing conclusions regarding enterotype presence if they apply different criteria, even when analys led to a modified concept, emphasizing the same data. To enhance the practical utility of the enterotype concept,need for standardization in enterotyping methods is necessary, particularly to benefit microbial ecologists and clinicians interested in this field [130]. However, some promising studies suggest that enterotypes may aid in predicting dietary responses.

IEn 2011, Wu et al. were among the first to explore the connections between dietary variables and enterotypes. They conducted a cross-sectional analysis with 98 healthy volunteers, obtaining 16S rRNA sequencing data from stool samples. They collected dietary information from participants through dietary recalls and evaluated long-term habits using a food frequency questionnaire. Additionally, they conducted a controlled-feeding study involving ten individuals following either a high-fat/low-fiber or a low-fat/high-fiber diet for 10 days. Following the methodology introduced by Arumugam et al., the researchers explored the potential categorization of the study population into distinct clusters. Various statistical methods were employed, with most indicating only two groupings where Bacteroides anterotypes, particularly the Prevotella-dominated (P-type) and Bacteroides-dominated Ruminococcus en(B-terotypes merged. The feeding study demonstrated microbiome composition changes within 24 h, while the enterotype identity remained constant during the intervention [17]. This effect seems to apply not only to short-term studies but also to long-term studies. A 6-month randomized controlled intervention study confirmed this result. They instructed sixty-two obese subjects, aged between 18 and 65, to choose between following the new Nordic diet recommendations or adhering to the average Danish diet, and no alterations in the enterotypes were detectable [119]. Notably, enterotypes strongly correlate with indi) types play a crucial role in reflecting individuals’' long-term dietary patterns. The Prevotella-dominated enterotype (P-type), in humans correlatesassociated with vegetarians, high-fiber and carbohydrate-rich diets, and traditional diets. Conversely, individuals who consume diets rich in animal fats and proteins, typical in Western-style diets, often exhibit the Bacteroides-dominated enterotype (B-type). The functional differences bet, shoween the two enterotypes emphasize this observation. The P-type showcases efficient hydrolytic enzymes for plant fiber degradation but has limited lipid and protein fermentation capacity. ConverselyIn contrast, the B-type features specialized enzymes tailored for the degradation of animal-derived carbohydrates and proteins, showcasexhibiting an enhanced saccharolytic and proteolytic potential [127,131,132]^[62][63][64]. In vi

Stro studies have also observed functional distinctions, particularly when examining dietary fibers of different chemical structures. Upon exposure to arabinoxylans derived from grain bran, fecal samples associated with the P-type enterotype demonstrate elevated production of short-chain fatty acids, notably propionate, in contrast to the B-type samples [118]. P-type specimens exhibit a diminished capacity for growth when subjected to primary carbohydrate substrates, such as soluble starch, pectin, and xylan, in stark contrast to their B-type counterparts [133].

Researchers are conducted by a research group from Denmark highlight the functional differences between Prevotella and Bacteroincrdeasingly basing their investigations on these findings. A research group from Denmark observed these functional differences in a series of hus in various human dietary intervention studies. They report that the Prevotella t Prevotella to Bactero Bacteroidesides ratio (P/B ratio) iwas closely related to alterations in body fat [134–136] and weight [135–139] (Table 1). A , with a caloric deficit of 500 kcal for 24 weeks ledading to more significant weight and body fat loss in individuals with a high P/B ratio, while weight loss significantly correlated with fiber intake [135]. Similar . This group also observations were made by Zou et al. A 3-week calorie restriction also resulted in a higher BMI loss in P-type individuals [140]. A high-ed that a high-fiber diet, characteristic of traditional dietary patterns in P-type individuals, likely results in more efficient and substantial weight loss [134,136–139,141]^{[65][66][67][68][69][70]}. KovatcTheva-Datchary et al. additionally demonstrated that a barley kernel had a positive impact on glucose and insulin metabolism in high-Prevotella individuse functionals. A metagenomic analysis confirmed this by revealing that Prevotella copri exhdistibited an increased potential for fermenting complex polysaccharides after the intervention [142]. These studies focused on the differential response to dietary interventions concerning anthropometric, metabolic, and metagenomic outcomes. Investigations examining the effects of enterotypes on metabolic responses using metabolomics approaches are still uncommon. A large-scale cross-sectional study by Wu et al. (n = 1199) used a pctions underscore the relevance of considering indipeline to predict metabolic microbial functions. P-type individuals showed elevated metabolic activity involving propanoate, starch, and sucrose, which aligns with findings from in vitro studies. On the other hand, B-type individized dietary plans based on enterotypes.

Cuals rrexhibited improved fatty acid metabolism [143]. Kang et al. also employed the pipeline to predict metabolomic microbial functions and examined SCFAs in fecal samples. They also measured incretin hormones—GLP-1 and gastric inhibitory polypeptide (GIP)—and the hunger hormone ghrelin in twelve non-obese adults who followed a low- and high-capsaicin diet for 6 weeks. The response to the intervention varied depending on the enterotype. The intervention increased GLP-1 and GIP concentrations while decreasing ghrelin concentrations in P-type individuals. Higher GLP-1 and GIP concentrations are associated with increased satiety and improved insulin and glucose metabolism. Reduced ghrelin levels are also associated with decreased hunger and increased satiety. In B-types, the intervention resulted in higher Faecalibacterium abnt research offers promising insights into personalized dietary recommendations groundances and higher butyrate concentrations [110]. In this case, a metagenomic study providing information on the pathways of both ented in enterotypes and an additional examination of SCFAs in plasma for more insights into systemic effects would be helpful. Metmetabolomic profiling of serum and urine samples was applied in a study by Shin et al., comparing three different dietary forms in a 4-week cross-intervention study. In P-type individuals, isoleucine levels decreased after traditional nutrition, suggesting an altered BCAA metabolism. After the recommended American diet, acetate concentration in P-types increased, which might result from sufficient fiber intake. Serum carnitine levels increased significantly only in B-type individuals, which suggests an adapted metabolism for animal-based foods. An elevated concentration of urinary dimethylamine, likely resulting from the breakdown of carnitine, further supported this observation [144]. Hur et al. conducted an untargeted and targeted metabolomic analysis of serum samples. The traditional diet led to a reduction in body weight in both P-type and B-type. However, this decrease in body weight was associated with a loss of muscle mass in P-type but not in B-type. Furthermore, the traditional diet led to reduced serum lipid and amino acid concentrations in comparison to the control diet in both enterotypes. The decline in these concentrations was more significant in the P-type than in the B-type [141].

Overall, thes methods. Despite the challenges stemming from microbiome intricacies, advancements in AI and machine learning tools exhibit potential in predicting metabolic response studies indicate that there are already promising insights into personalized dietary recommendations based on the composition of the gut microbiome. It seems that P-type individuals’ metabolism may respond more favorably to a fiber-rich diet. Nonetheless, only a few studies support this hypothesis using a metabolomics approach, for example, by measuring SCFAs in various tissues. These include compounds such as TMAO and the metabolic byproducts of amino acid and fatty acid fermentation, and the metabolism of secondary bile acids. Identifying metabolites using metabolomics methods also has its challenges, as previously discussed. Categorizing individuals into enterotypes is a noteworthy simplification of the intricate micr to specific diets. The future trajectory involves surmounting challenges in comprehending microbiome complexity, presenting unique technical challenges. In addition to the genera Prevotella afunctions and Bacteroides, the functions and distribution of other bacterial species, along with factors such as transit time and microbial diversity, are pivotal in influencing both health outcomes and the production of microbial metabolites. Furthermore, it is essential to acknowledge that classifying based on a particular genus may not encompass the functions of various strains and clades within bacterial genera, which can exhibit substantial variations and remain unaccounted for in the context of enterotype classification [124]. Creating personalized diet recommendations informed by the microbiome presents a formidable challenge. Human studies encounter complexities stemming from substantial individual variations, the constrained ability to manipulate microbiome composition, and the practical difficulties associated with adhering to experimental dietary regimens. To address these challenges, studies on human nutrition require large participant cohorts, and some metabolic changes necessitate long-duration experiments, which can often be unrealistic. Researchers are addressing this intricate challenge by increasingly relying on AI and machine learning tools. These advanced tools scrutinize data concerning food composition, the mirnessing these advanced tools for more precise predictions. This ongoing effort holds the promise of enhanced dietary guidance tailored to individual microbiome, and human physiological responses, employing this information to forecast compositions.

In the collective impact of these elements on particular outcomes. These models excel at predictions but often struggle to fully elucidate why certain substances yield varied responses. However, when researchers train these algorithms with suitable data, the algorithms can identify essential factors and make accurate predictions about metabolic responses.

Table 1. Characteristics onclusion, the integration of studies exploring ennterotypes and dietary associations.

In this table, words in bold highlight studies that used a metabolomics approach.

5. Conclusions

In conclusrovion, these studies provide ves valuable insights into the potential for personalized dietary recommendations based on enterotypes and metabolomics methodsan individual's unique microbiome composition. Despite the challenges associated with compreheinherent in understanding the intricacies of the microbiome and its multifaceted functions, the application of metabolomics offers a emerges as a promising avenue for deciphering these complexities. These findings pave the waylay the foundation for more nuanced and tailored dietary guidance, offering the potential for improved metabolic responses based on an individual’s unique's distinct microbiome composition.