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Recent advances in high-throughput DNA sequencing have catalyzed a deeper exploration of the human microbiome and its intricate relationship with metabolic health. Researchers examine the influence of dietary choices on the production of microbial metabolites and provide a comprehensive review of studies investigating the interplay between enterotypes and diet. The goal is to contribute to the refinement of personalized dietary recommendations and provide valuable insights to advance the understanding of metabolic health.
Over the past two decades, research into the human microbiome and its link to metabolic health has been driven by advances in high-throughput DNA sequencing. Studies have revealed associations between changes in gut microbiota composition and metabolic disorders such as obesity [1][2], Type 2 Diabetes Mellitus (T2DM) [3], and cardiovascular diseases [4][5]. Influencing factors include age [6], gender [7], geography [8], genetics [9], and environmental elements [10][11][12], with dietary choices [13] playing a significant role.
To comprehend the intricate relationship between dietary patterns, the gut microbiome, and their effect on host metabolism, researchers have introduced enterotypes as a classification framework [14]. These categorize individuals based on their gut microbiota composition. To unravel the functional implications of enterotypes on host metabolism, it is necessary to integrate metabolomics, a discipline that studies small molecular compounds. Metabolomics provides a comprehensive overview of the metabolites produced by the gut microbiota in response to diet.
The Human Microbiome Project initiative (HMP) has been instrumental in establishing fundamental frameworks for microbiota analysis [15]. Specifically, amplicon-based sequencing of 16S ribosomal ribonucleic acid (rRNA) has been widely used to analyze microbiome composition.
Key figures, such as those involved in the HMP initiative, have made significant contributions. The shift from amplicon-based sequencing to metagenomic analyses represents a milestone, as demonstrated by influential projects such as MetaHIT [16]. Metagenomic methods not only reveal taxonomic composition but also offer insights into functional metabolic capabilities, such as identifying genes that encode enzymes responsible for breaking down food components.
Metaproteomics in microbiome research was introduced in 2008 by Verberkmoes et al [17]. This technique enables a detailed investigation of microbial proteins and links them to specific microorganisms. Furthermore, Booijink et al. made a significant advancement by initiating a metatranscriptome analysis of the human fecal microbiome, which provides tractable links between microbial genetic potential and molecular activity [18].
The seamless progression from amplicon-based sequencing through metagenomics to metaproteomics and metatranscriptomics has established a comprehensive foundation for exploring the functional aspects of the microbiome. To gain a truly holistic understanding of its role in human health, the focus shifts further into the realm of metabolomics. Metabolomic techniques offer insight into the overall metabolic status and interactions within the microbiome. Despite the diverse characteristics of metabolites, metabolomics employs various technologies, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, to measure defined sets of known metabolites or perform a comprehensive analysis of the metabolome [19].
As part of this progression, the use of mass spectrometry (MS), particularly gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), has become more prevalent in scientific studies. MS has the ability to detect metabolites with high sensitivity, impartiality, and efficiency [20]. Metabolomics, when combined with artificial intelligence (AI) and machine learning (ML) techniques, has the potential to enhance the understanding of the microbiome's significance for personalized biomarker discovery, and nutritional recommendations [21].
Ethical considerations in metabolomics research are of utmost importance, emphasizing the need for privacy, informed consent, and transparency in handling participant data. Collaboration among researchers, ethicists, policymakers, and healthcare professionals is essential to address ethical concerns, foster responsible advancement, and maximize the benefits of metabolomics, microbial research, and personalized nutrition for society [22][23].
The investigation of the human microbiome has experienced significant expansion, with a specific focus on the intricate relationship between diet, the gut microbiome, and microbial metabolites. This interplay substantially influences the overall health, as nutrients impact microbial growth, leading to the production of diverse metabolites.
The utilization of carbohydrates and dietary fiber holds particular importance. Microbiota-accessible carbohydrates (MACs), primarily obtained from plant-based sources, are indigestible carbohydrates that play a pivotal role in the interaction between the human body and the microbiome [24][25]. The microbiome possesses carbohydrate-active enzymes (CAZymes) that break down complex dietary carbohydrates, resulting in the production of short-chain fatty acids (SCFAs) as the primary end products. SCFAs, comprising acetate, propionate, and butyrate, exert various effects on human metabolism, influencing gut barrier integrity [26], gut motility [27], hormone regulation [28][29][30][31], chromatin dynamics [32][33], gut-brain connections [34], and immune responses [35]
In the absence of fermentable fibers, gut microbes adapt by utilizing amino acids from dietary proteins [36][37][38]. Proteolytic bacteria, including Bacteroides and Clostridium species, contribute to amino acid fermentation, yielding SCFAs and branched-chain fatty acids [38][39]. The breakdown of branched-chain amino acids (BCAAs) like valine, leucine, and isoleucine, associated with an impaired insulin resistance, highlights the intricate relationship between protein fermentation, microbial metabolites, and metabolic health [20][40][41].
Contrary to previous beliefs, dietary fat significantly influences the composition of the gut microbiota, subsequently impacting the host's health [42]. A diet rich in saturated fats can induce metabolic endotoxemia [43], a pro-inflammatory condition mediated through toll-like receptor 4 (TLR4) and CD14, leading to outcomes like weight gain, increased adiposity, and insulin resistance [44]. Bile acids, crucial for fat digestion and absorption, also engage with the gut microbiota [45]. An imbalance in the gut microbiota can perturb the bile acid receptor farnesoid X receptor (FXR), potentially contributing to conditions such as colorectal cancer [46].
Plant-derived polyphenols, abundant in various dietary sources, undergo significant modifications by the gut microbiota, influencing their absorption and bioavailability [47]. Despite limited bioavailability, polyphenols exhibit anti-inflammatory, antioxidant, and antimicrobial properties, impacting conditions such as cardiovascular disease [48], cancer [49], metabolic disorders [50], Alzheimer’s disease [51], and inflammatory bowel disease [52]. Additionally, nutrients from animal-derived foods, like choline, betaine, and l-carnitine, interact with the gut microbiome, contributing to the formation of trimethylamine-N-oxide (TMAO) and influencing health outcomes [53][54].
This overview highlights the importance of microbial metabolites in the relationship between dietary components and human health when exploring diet-microbiome interactions.
Understanding the intricate relationship between an individual's microbiota and their response to dietary elements highlights the impracticality of a universal dietary approach. Recognizing the significance of precision nutrition becomes imperative in designing tailored dietary plans, mitigating the challenges of variability inherent in nutrition research. This chapter explores the concept of enterotypes, a categorization based on gut microbiota composition, shedding light on their applications, current status, and future directions in personalized dietary recommendations.
The concept of enterotypes was introduced in 2011 by Arumgam et al., who identified three distinct clusters based on microbiota composition across diverse populations [14]. Subsequent studies, including those by Liang et al., reinforced the presence of enterotypes and revealed variations, particularly in Asian populations [55]. However, challenges arose regarding the definition and identification of enterotypes, prompting further investigation.
Enterotypes, initially characterized by dominant genera like Bacteroides and Prevotella, have shown consistent patterns across various populations [56][57][58]. Although regional dietary distinctions likely contribute to subgroup variations within enterotypes, both Bacteroides and Prevotella enterotypes exhibit equal associations with diverse health conditions [59][60]. Notably, the role of Bifidobacterium in the Bacteroides enterotype, particularly in Japan, underscores the influence of regional dietary habits [61].
The prominence of enterotypes lies in simplifying the complex gut microbiome landscape, offering avenues for microbiota-based diagnostics, therapeutic interventions, disease prevention strategies, and personalized dietary recommendations. However, challenges in precisely defining enterotypes led to a modified concept, emphasizing the need for standardization in enterotyping methods.
Enterotypes, particularly the Prevotella-dominated (P-type) and Bacteroides-dominated (B-type) types play a crucial role in reflecting individuals' long-term dietary patterns. The P-type, associated with vegetarians, high-fiber and carbohydrate-rich diets, and traditional diets, showcases efficient hydrolytic enzymes for plant fiber degradation but has limited lipid and protein fermentation capacity. In contrast, the B-type features specialized enzymes tailored for the degradation of animal-derived carbohydrates and proteins, exhibiting an enhanced saccharolytic and proteolytic potential [62][63][64].
Studies conducted by a research group from Denmark highlight the functional differences between Prevotella and Bacteroides in various human dietary intervention studies. The Prevotella to Bacteroides ratio (P/B ratio) was closely related to alterations in body fat and weight, with a caloric deficit of 500 kcal for 24 weeks leading to more significant weight and body fat loss in individuals with a high P/B ratio. This group also observed that a high-fiber diet, characteristic of traditional dietary patterns in P-type individuals, likely results in more efficient and substantial weight loss [65][66][67][68][69][70]. These functional distinctions underscore the relevance of considering individualized dietary plans based on enterotypes.
Current research offers promising insights into personalized dietary recommendations grounded in enterotypes and metabolomics methods. Despite the challenges stemming from microbiome intricacies, advancements in AI and machine learning tools exhibit potential in predicting metabolic responses to specific diets. The future trajectory involves surmounting challenges in comprehending microbiome functions and harnessing these advanced tools for more precise predictions. This ongoing effort holds the promise of enhanced dietary guidance tailored to individual microbiome compositions.
In conclusion, the integration of enterotypes and metabolomics provides valuable insights into the potential for personalized dietary recommendations based on an individual's unique microbiome composition. Despite the challenges inherent in understanding the intricacies of the microbiome and its multifaceted functions, the application of metabolomics emerges as a promising avenue for deciphering these complexities. These findings lay the foundation for more nuanced and tailored dietary guidance, offering the potential for improved metabolic responses based on an individual's distinct microbiome composition.