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Article

Key Stratification of Microbiota Taxa and Metabolites in the Host Metabolic Health–Disease Balance

by
Alfonso Torres-Sánchez
1,
Alicia Ruiz-Rodríguez
1,2,3,*,
Pilar Ortiz
2 and
Margarita Aguilera
1,2,4,*
1
Department of Microbiology, Faculty of Pharmacy, Campus of Cartuja, University of Granada, 18071 Granada, Spain
2
Institute of Nutrition and Food Technology “José Mataix” (INYTA), Centre of Biomedical Research, University of Granada, 18016 Granada, Spain
3
Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, Campus of Cartuja, University of Granada, 18071 Granada, Spain
4
Instituto de Investigación Biosanitaria (IBS), 18012 Granada, Spain
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(5), 4519; https://doi.org/10.3390/ijms24054519
Submission received: 31 January 2023 / Revised: 21 February 2023 / Accepted: 22 February 2023 / Published: 24 February 2023
(This article belongs to the Special Issue Microbiome and Metabolome in the Gastrointestinal Tract)
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Abstract

:
Human gut microbiota seems to drive the interaction with host metabolism through microbial metabolites, enzymes, and bioactive compounds. These components determine the host health–disease balance. Recent metabolomics and combined metabolome–microbiome studies have helped to elucidate how these substances could differentially affect the individual host pathophysiology according to several factors and cumulative exposures, such as obesogenic xenobiotics. The present work aims to investigate and interpret newly compiled data from metabolomics and microbiota composition studies, comparing controls with patients suffering from metabolic-related diseases (diabetes, obesity, metabolic syndrome, liver and cardiovascular diseases, etc.). The results showed, first, a differential composition of the most represented genera in healthy individuals compared to patients with metabolic diseases. Second, the analysis of the metabolite counts exhibited a differential composition of bacterial genera in disease compared to health status. Third, qualitative metabolite analysis revealed relevant information about the chemical nature of metabolites related to disease and/or health status. Key microbial genera were commonly considered overrepresented in healthy individuals together with specific metabolites, e.g., Faecalibacterium and phosphatidylethanolamine; and the opposite, Escherichia and Phosphatidic Acid, which is converted into the intermediate Cytidine Diphosphate Diacylglycerol-diacylglycerol (CDP-DAG), were overrepresented in metabolic-related disease patients. However, it was not possible to associate most specific microbiota taxa and metabolites according to their increased and decreased profiles analyzed with health or disease. Interestingly, positive association of essential amino acids with the genera Bacteroides were observed in a cluster related to health, and conversely, benzene derivatives and lipidic metabolites were related to the genera Clostridium, Roseburia, Blautia, and Oscillibacter in a disease cluster. More studies are needed to elucidate the microbiota species and their corresponding metabolites that are key in promoting health or disease status. Moreover, we propose that greater attention should be paid to biliary acids and to microbiota–liver cometabolites and its detoxification enzymes and pathways.

1. Introduction

Gut microbiota is considered a complex ecosystem with a wide array of microorganisms linked to host health. Multiple studies suggested that the structure and composition of the gut microbiota in metabolic-related diseases, such as atherosclerosis, colitis, diabetes, hyperlipidemia, hypertension, metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), obesity, and steatosis, exhibit significant changes compared to healthy individuals and that those changes are related to host physiopathology. In this context, the analysis and description of trends in microbial populations associated with disease and health status become a key issue to elucidate possible signatures of metabolic-related diseases.
The gut microbiota of patients with metabolic-related diseases shows differences at different taxonomic levels. Many studies showed that Parabacteroides, Bifidobacterium, Oscillospira, and Bacteroides were decreased in patients with obesity [1,2,3,4,5,6,7,8,9,10,11,12,13]. Moreover, Faecalibacterium and Bifidobacterium were decreased [14,15,16,17,18,19,20,21] and species from Lactobacillaceae family [22] and Blautia were increased [7,13,19,20,21,22,23,24,25,26,27] in diabetic patients. Other metabolic diseases related to intestinal diseases seem to be related to increased Escherichia and decreased Faecalibacterium [28,29,30,31,32,33,34,35,36,37].
Recently, the combination of metagenomics and metabolomics has received extensive attention due to the growing number of studies that establish positive and negative correlations between gut microbiota taxa, metabolites, and health status. Therefore, future studies will contribute to elucidate the essential role of gut microbiota in metabolite synthesis, metabolite modifications, and metabolic pathway regulations.
In this sense, metabolites such as short-chain fatty acids (SCFA), amino acids (AA), or bile acids (BA) can play a crucial role in maintaining metabolic functions or, on the contrary, they might be involved in disease development, such as choline derivatives in the case of cardiovascular diseases [38,39,40,41]. Metabolite influences are not restricted to the intestine and distribution to other physiological locations has been described through different axes, such as the gut–liver axis, in which the gut microbiota is related to liver diseases, including NAFLD, NASH, fibrosis, or liver cancer [42]. Gut microbiota partially impacts the host BA profile as it is involved in primary bile acid transformation into secondary free bile acids, such as deoxycholic acid, lithocholic acid, and ursodeoxycholic acid, contributing to the modulation of host total bile acid production [43].
The chemical structure of many endogenous compounds, including gut microbiota metabolites, can be modified, resulting in changes in their bioactivity and half-life [44]. This kind of modifications are related to the development of complex metabolic networks between host and gut microbiota, where final substances could be potentially more toxic than the original ones [45].
Traditional probiotics, mainly consisting of species from Lactobacillaceae and Bifidobacteria and a few from other genera, have been largely applied as a useful strategy in the context of clinical intervention in metabolic-related diseases [46,47]. However, the development of new procedures using Next Generation Probiotics (NGP) opens a new world of possibilities due to the beneficial effects that have already been described in murine models and, to a lesser extent, in humans. In this context, murine models show Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides uniformis, Bacteroides acidifaciens, Clostridium butyricum, and Prevotella copri as interesting microorganisms with potential applications in obesity [48,49,50,51,52,53], liver diseases [52,54,55,56,57,58,59], diabetes [48,49,50,51,52,53,58,60,61], colitis [62], and hyperlipidemia [53,58].
This work will contribute to finding out microbial and metabolite patterns and their correlation with diseases that have been studied independently or not yet extensively studied. Therefore, the principal aim of this work is to identify and describe the association between human gut microbiota taxa changes in metabolic-related diseases, incorporating the correlations with metabolites, and how they can modulate host health.

2. Results

2.1. Differential Microbiota Taxa Composition and Stratification According to Their Representation in Metabolic Diseases

2.1.1. PRISMA Analysis

Gut microbial taxa differences in diabetes, obesity, metabolic syndrome, and liver and cardiovascular diseases, highlight links between gut microbiota and host health status. In this context, Figure 1 summarizes updated and available information about gut microbial taxa changes in these metabolic-related diseases.

2.1.2. Microbial Taxa Decreased in Patients Suffering from Metabolic-Related Diseases

Increased and decreased trends in gut microbiota taxa were assessed through an extensive literature search including information about metabolic diseases investigated by different authors. In this context, the approach we followed offered some drivers of specific changes in gut microbiota composition that could be related to host health.
The analysis of 75 studies involving changes of the main taxa altered in patients suffering metabolic-related diseases disclosed 121 differentially abundant microbial genera (complete data are available in Supplementary Material S1). Figure 2 shows representative genera count value comparison obtained in metabolic diseases after microbial taxa variation analysis.
Gut microbiota genera such as Oscillibacter, Butyricicoccus, Odoribacter, and Paraprevotella were exclusively decreased in individuals affected by metabolic diseases. On the other hand, Faecalibacterium, Bifidobacterium, Ruminococcus, Parabacteroides, Roseburia, Akkermansia, Alistipes, Coprococcus, and Oscillospira were both decreased and increased in metabolic-related diseases. However, overall, these microbial genera showed a negative association with the metabolic diseases studied here.

2.1.3. Microbial Taxa Increased in Patients Suffering Metabolic-Related Diseases

Microbial genera such as Klebsiella, Collinsella, and Enterococcus were exclusively present in those cases in which individuals were affected by metabolic diseases. However, taxa belonging to Escherichia, Lactobacillaceae, Blautia, Streptococcus, and Dorea were also identified in patients without metabolic-related diseases. These microbial genera showed an upward trend in metabolic-related diseases studied here. Figure 3 shows the distribution of representative microbial taxa linked to metabolic-related diseases.
In a previous study exploring next generation probiotics for metabolic and microbiota dysbiosis linked to xenobiotic exposure [63], we tried the first approach to describe changes in gut microbial taxa associated to metabolic-related disease. As a result, potential associations between bacterial genera and metabolic diseases were described despite the lesser number of analyzed studies. In this case, Table 1 shows an expansion of the current knowledge available in this field, including the relevant information identified in the previous study.

2.2. Differential Microbial Metabolites and Stratification According to Their Representation in Metabolic Diseases

The analysis of the 16 selected studies involving correlations between gut microbiota taxa altered in patients suffering from metabolic diseases, metabolites, and host health status allowed us to shed light on potential critical pathways to modulate homeostatic processes (complete data are available in Supplementary Material S2 [103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118] Figure 4 summarizes available information about gut microbiota–metabolite correlations and host health status.
Several gut microbiota taxa showed a high metabolite count linked to disease or health status. In that regard, increased microbial metabolite counts in health status were obtained in gut microbiota genera such as Holdemania, Porphyromonas, and Dialister; further, they were also higher for Bacteroides, Clostridium, and Alistipes, but with more similar counts in both groups. Figure 5 shows representative genera differential values associated to health-related metabolite count analysis.
Increased metabolite counts related to disease status were linked to gut microbiota taxa such as Ruminococcus, Eubacterium, Blautia, Roseburia, Oscillibacter, Subdoligranulum, Gemmiger, Butyricicoccus, Akkermansia, Veillonella, Dorea, Coprococcus, Escherichia, Parabacteroides, Enterobacter, Lachnospira, Gemella, and Fusobacterium. Figure 6 shows representative genera differential values associated to disease-related metabolite count analysis.
According to the total metabolites linked to disease and health status, 171 metabolites were associated with metabolic-related diseases; among these, 143 were exclusively associated with this group and 28 were shared with health status. Moreover, 63 metabolites were related to health status, and 35 were exclusively associated with this group. A qualitative metabolite analysis was performed considering total disease/health-related metabolites. Table 2 shows disease/health-related metabolites classified according to three main chemical groups: fatty acids and conjugates, amino acids and derivatives, and bile acids and derivatives.
A further association analysis of the number of studies where a specific association between a metabolite and a bacterial genus was found showed very interesting clustering patterns. For instance, butyrate-producer genera when present in a healthy status associated with bile acid metabolites and, to a lesser extent, with essential amino acids; however, when they are overrepresented in metabolic diseases, they are associated with lipid metabolism, clustering in two distinct groups. We also observed that essential amino acids clustered together, and they might have an important role for the metabolism of Bacteroides in health status, according to Figure 7.

3. Materials and Methods

We performed a comprehensive literature search covering the period from 1995 to November 2022 using Scopus, Web of Science, and PubMed databases, using the search strategies showed in systematic review and dividing this review into two main study issues: gut microbial taxa variations in metabolic-related diseases and gut microbiota–metabolite correlations in metabolic-related diseases.
Studies involving changes in gut microbial taxa in atherosclerosis, colitis, diabetes, hyperlipidemia, hypertension, metabolic syndrome, NAFLD, NASH, obesity, and steatosis and studies involving microbiota–metabolite correlations in metabolic-related diseases were assessed, screened, and selected according to PRISMA 2020 flow diagrams (Figure 1 and Figure 4) [111].
In the microbial taxa variation analysis, gut microbial taxa identified in selected studies were divided into two groups: decreased in metabolic-related diseases and increased in metabolic-related diseases, based on research findings. Metabolite counts were calculated for each microbial genus. To determine representative gut microbiota taxa, an arbitrary criterion was applied. Microbial genera were considered representative if the absolute frequency difference between decreased–increased counts was greater than three.
In the gut microbiota–metabolite correlation analysis, gut microbiota, microbial metabolites, and host status correlations were assessed. First, gut microbial genera were classified into increased in health status or increased in diseases, according to metabolite absolute frequencies displayed for each genus. Second, considering metabolites related to representative genera in health or disease status, a qualitative metabolite analysis was performed. Metabolites correlated with health or disease status were classified into three main groups: fatty acids and conjugates (FA), amino acids and derivatives (AA), and bile acids and derivatives (BA), according to PubChem and related chemical database classification. Furthermore, a bioinformatics analysis was performed to establish potential biomarkers, which revealed the association between specific disease/health balances. Heatmap shows the analysis where a specific association between a metabolite and bacterial genera was found in a health and/or a disease stage (as indicated by “_H” or “_D”, respectively). For simplicity, only the representative genera and the most found metabolites (metabolites that appeared least five times either associated with health or disease in the studies analyzed here) were included. First, we selected only the genera with more than 10 metabolites associated and then we kept only the metabolites that appeared at least five times, either associated with health or disease, in the studies analyzed here. Figure 7 shows the performance of R (version 4.1.1.) using the package “pheatmap” [112].

4. Discussion

There is a growing interest in the analysis of the gut microbiome and its metabolome [113,114]. However, integrating data from both fields to understand how gut microbiota, microbial metabolites, and host status are correlated not always provide concise information. Thus, it can hinder researchers in establishing clear links between the presence of a particular gut bacterial taxa and/or metabolites and disease or health status. This task is especially challenging in the context of searching gut microbial biomarkers that allow predicting future phenotypes or classifying individuals into disease and non-disease status. This is mainly due to the fact that contradictory results about microbial taxa abundance and metabolites related to disease or non-disease status can be found in the literature. In this case, this approach showed that Faecalibacterium, Bifidobacterium, Ruminococcus, Parabacteroides, Roseburia, Akkermansia, Alistipes, Coprococcus, Oscillospira, Oscillibacter, Butyricicoccus, Odoribacter, and Paraprevotella could represent a downregulated microbial cluster in metabolic-related disease patients and, on the contrary, Escherichia, species from Lactobacillaceae family, Blautia, Streptococcus, Klebsiella, Collinsella, Dorea, and Enterococcus cluster upregulation could be involved in metabolic-related disease status. Due to relevant information underlined by many authors and results obtained in this review, Ruminococcus and Bifidobacterium, as well as taxa belonging to Lactobacillaceae family, Blautia, and Dorea should be identified at the species level to establish similarities with the results already available in the microbiological databases.
According to metabolite absolute frequencies in disease and health status and representative gut microbiota taxa, we tried to search for possible trends between those elements and host physiopathology. When we compared representative metabolites and microbial taxa results, only Alistipes, from the down-regulated proposed cluster, showed high counts in both gut microbial taxa variation analysis and metabolite count analysis related to health. In the same way, Escherichia, Blautia, Streptococcus, Collinsella, Dorea, and Enterococcus, from the proposed upregulated cluster, showed high counts in both gut microbial taxa analysis and metabolite count analysis in disease/disorder group.
Following this approach, Faecalibacterium and Akkermansia genera [115,116], frequently described as key microorganisms related to health status, were decreased in metabolic-related diseases, indicating a possible relationship with health status. However, a link with disease status could be identified according to metabolite absolute frequencies described for both genera Faecalibacterium and Akkermansia. A similar result can be observed in other microorganisms frequently associated with metabolic diseases [117], where microbial taxa analysis showed links with obesity-related diseases. However, metabolite absolute counts showed links with health status.
Interestingly, preliminary data results derived from the biomarker search have demonstrated the positive association of essential amino acids with health in the genera Bacteroides, and conversely, benzene derivatives have been related to disease and the genera Clostridium. We also observed that lipid metabolites grouped several taxa overrepresented in diseases, but it will be necessary to determine the results to the species level.
These results showed which bacterial taxa of the gut microbiota and their derived metabolites could be related to host status manifestations. However, study limitations and lack of available data in some fields make it impossible to establish final and solid conclusions in this way.
Human health is not only affected by gut microbiota composition and its derived metabolites but also many exogenous and endogenous factors, which can also impact in genotypic and phenotypic manifestations. Recently, the holistic concept of the One Health approach and the exposome include multidisciplinary analysis of a complex reality that affect different but linked items [118]. Nowadays, solid evidence about specific microbial and metabolite signatures in cases of metabolic-related disease is still limited and more concrete information on the correlations between gut microbiota, gut metabolites, and host health status is needed. This synergic approach will lead to a better management of well-known microbiota–metabolic related diseases.
To increase the availability of scientific data on the interaction between gut microbiota taxa in different health contexts, metabolite synthesis, and metabolite modification and impact on the host health, integrated metagenome and metabolome analysis should be continually reviewed, since it seems to be a possible cornerstone involved in the determination of potential microbial and metabolite signatures related to physiological alterations.

5. Conclusions

Despite the existence of microbial taxa–metabolite-health correlations, there is no evidence of a clear gut microbiota and derived metabolite patterns into healthy or metabolic-related disease status that is able to predict or classify patients into one or the other.
Most of the taxa and metabolites did not show representative oscillations between disease and health groups, so bacterial genera with potential interest should continue to be monitored as new information on their abundance in metabolic-related disease appearance.
Implementation of the One Health holistic approach combined with exposome principles can provide new perspectives and evidence about how endogenous and exogenous substances interact with gut microbiota and microbial-derived substances and how the pull of interactions finally affects human homeostasis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24054519/s1.

Author Contributions

Conceptualization, A.T.-S. and M.A.; Methodology, A.T.-S.; Writing—Original Draft Preparation, A.T.-S.; Writing—Review and Editing, P.O., A.R.-R. and M.A.; Supervision, A.R.-R. and M.A.; Project Administration, M.A.; Funding Acquisition, M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was carried out within the framework of the project and in the framework of several projects: FEDER Project Infrastructure: IE_2019-198; Junta de Andalucía Proyectos de Excelencia: Consejería de Universidad, Investigación e Innovación P21-00341 and the project Instituto de Salud Carlos III-PI20/01278. A.T.-S. holds a contract from FIBAO. A.R.-R. holds a Maria Zambrano Talent Grant (Next Generation EU-University of Granada).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Part of the results are from the doctoral thesis of Alfonso Torres-Sánchez in the Nutrition and Food Technology Doctorate Programme of the University of Granada.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. PRISMA diagram for gut microbial taxa changes in metabolic diseases.
Figure 1. PRISMA diagram for gut microbial taxa changes in metabolic diseases.
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Figure 2. Analysis of main taxa stratified according to high representativeness in patients without metabolic-related diseases.
Figure 2. Analysis of main taxa stratified according to high representativeness in patients without metabolic-related diseases.
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Figure 3. Analysis of main taxa stratified according to high representativeness in metabolic−related diseases patients.
Figure 3. Analysis of main taxa stratified according to high representativeness in metabolic−related diseases patients.
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Figure 4. PRISMA diagram for gut microbiota–metabolite correlations and host status.
Figure 4. PRISMA diagram for gut microbiota–metabolite correlations and host status.
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Figure 5. Health−related metabolite counts stratified according to gut microbiota taxa producers.
Figure 5. Health−related metabolite counts stratified according to gut microbiota taxa producers.
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Figure 6. Disease−related metabolite counts stratified according to gut microbiota taxa producers.
Figure 6. Disease−related metabolite counts stratified according to gut microbiota taxa producers.
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Figure 7. Heatmap showing the analysis where specific associations between a metabolite and a bacterial genus was found in a health and/or a disease stage (as indicated by “_H” or “_D”, respectively). For simplicity, only the representative genera and the most found metabolites were included.
Figure 7. Heatmap showing the analysis where specific associations between a metabolite and a bacterial genus was found in a health and/or a disease stage (as indicated by “_H” or “_D”, respectively). For simplicity, only the representative genera and the most found metabolites were included.
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Table
Table 1. Changes in the main microbiota taxa found in patients suffering metabolic-related diseases.
Table 1. Changes in the main microbiota taxa found in patients suffering metabolic-related diseases.
Ref.Sample Size and Clinical TraitsGut Microbiota Taxa Modification
[1]n = 42; HC n = 21; OB n = 21↑ Prevotella, Megamonas, Blautia, and Fusobacterium, ↓ Alistipes, Faecalibacterium, Oscillibacter, Clostridium IV, XIVa, Barnesiella, Gemmiger, Parabacteroides, Coprococcus, Ruminococcus, and Bifidobacterium in OB
[2]n = 51; HC n = 30; OB/OW n = 21↑ Lactobacillus *, ↓ Bifidobacterium in OB/OW
[3]n = 51; HC n = 23; OB/OW n = 28↑ Faecalibacterium, Phascolarctobacterium, Lachnospira, Megamonas, and Haemophilus, ↓ Oscillospira, and Dialister in OB
[4]n = 192; HC n = 25; OW n = 22; OB n = 145↑ Escherichia coli, Pseudomonas, Fusobacterium, ↓ Bifidobacterium in OW/OB
[5]n = 143; HC n = 56; OB n = 87↑ Enterococcus, Blautia, Sutterella, Klebsiella, and Collinsella, ↓ Bacteroides, Parabacteroides, Anaerotruncus, and Coprobacillus in OB
[6]n = 78; HC n = 36; OB n = 42↓ Bacteroides in OB
[23]n = 66; HC n = 27; OB n = 17; OBT2D n = 22↑ Staphylococcus in OB; ↑ Lactobacillus * and Escherichia in T2D
[7]OW n = 34; OB n = 23; AbOB n = 53; Dys n = 78; IFG n = 21; IGT n = 3; T2D n = 21; HT n = 34↑ Serratia and Prevotella, ↓ Oscillospira in OW, OB, AbOB group; ↑ Blautia in T2D; ↑ Prevotella in HT
[8]n = 58; HC n = 15; OB n = 18; OB NAFLD n = 25↑ Phascolarctobacterium, Phascolarctobacterium succinatutens, Klebsiella, Klebsiella pneumoniae, Kluyvera, and Kluyvera ascorbata, ↓ Lactobacillus *, Oscillibacter, Ruminiclostridium, and Parabacteroides johnsonii in OB NAFLD; ↓ Alistipes, Paraprevotella, Bacteroides clarus, and Odoribacter splanchnicus in OB and OB NAFLD; ↓ Helicobacter, Helicobacter pylori in OB
[9]n = 73; HC n = 20; OB NAFLD n = 36; OB Non-NAFLD n = 17↑ Megasphaera, Lactobacillus *, and Acidaminococcus, ↓ Oscillospira, Eubacterium, and Akkermansia in OB NAFLD and OB Non-NAFLD; ↑ Streptococcus, ↓ Blautia, Alkaliphilus, and Flavobacterium in OB NAFLD
[10]n = 115; HC n = 54; OB n = 8; NAFLD n = 27; NASH n = 26↑ Bradyrhizobium, Anaerococcus, Peptoniphilus, Propionibacterium acnes, Dorea, and Ruminococcus, ↓ Oscillospira in NAFLD, NASH and OB vs. HC
[11]n = 23; HC n = 10; NASH n = 13↑ Lactobacillus * in (OB-NASH vs. LN-HC), (OB-NASH vs. OB-HC) and (OB-NASH vs. OW-NASH); ↑ Lachnospira in (OB-NASH vs. OB-HC); ↓ Roseburia in (OB-NASH vs. LN-HC) and (OB-NASH vs. OB-HC); ↓ Bifidobacterium in (OW-NASH vs. LN-HC); ↓ Faecalibacterium and Ruminococcus in (LN-NASH vs. LN-HC) and (LN-NASH vs. OB-HC); ↓ Ruminococcus in (LN-NASH vs. OB-NASH) and (LN-NASH vs. OW-NASH)
[64]n = 106; HC n = 38; OB n = 68 ↑ Clostridium in HT; ↑ Bacteroides in IGT
[12]n = 119; OB n = 69; Mets n = 50↑ Intestinibacter, Saccharibacteria genera incertae sedis, Clostridium sensu stricto, Romboutsia, Terrisporobacter, and Eggerthia, ↓ Rothia, Adlercreutzia, Parabacteroides, Paraprevotella, Alistipes, Bacteroides, Bilophila, Escherichia-Shigella, Lactobacillus *, Clostridium XIVa, Clostridium XIVb, Anaerotruncus, and Phascolarctobacterium in OB vs. Mets
[65]n = 60; HC n = 20; OB T2D n = 40↑ Eubacterium coprostanoligenes group, Dialister, and Allisonella, ↓ Ruminococcus 2, Prevotella 9, and Escherichia-Shigella 9 in OB T2D
[14]n = 1280; LN-NonT2D n = 633; OB-NonT2D n = 494; OBT2D n = 153↓ Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes in OB- NonT2D and OBT2D
[15]n = 50; HC n = 15; T2D n = 14; DR n = 21↑ Klebsiella and Enterococcus, ↓ Faecalibacterium and Lachnospira in T2D
[16]n = 154; CN n = 73; T2DCI n = 81↑ Peptococcus, ↓ Bifidobacterium, Veillonella, and Pediococcus in T2DCI
[66]n = 291; HC n = 193; T2D n = 98↑ Peptostreptococcus, Eubacterium, and Prevotella, ↓ Anaerostipes, Ruminococcus, Clostridium, Epulopiscium, Cellulosilyticum ruminicola, Clostridium paraputrificum, and Clostridium butyricum in T2D
[17]n = 60; HC n = 40; T2D n = 20↑ Streptococcus, Fusobacterium, and Dorea, ↓ Parabacteroides, Bifidobacterium, Faecalibacterium, and Akkermansia in T2D
[24]n = 102; HC n = 35; pT2D n = 17; NewT2D n = 11; KnownT2D n = 39↑ Escherichia and Acidaminococcus, ↓ Sutterella in KnownT2D; ↑ Megasphaera and Lactobacillus *, ↓ Akkermansia, Blautia, and Ruminococcus in NewT2D
[67]n = 118; HC n = 59; T2D n = 59↑ Bifidobacterium spp., ↓ Bacteroides spp. in T2D
[25]n = 100; HC n = 35; T2D+ n = 49; T2D− n = 16↑ Coprococcus 1, ↓ Bacteroides and Prevotella in T2D+ and T2D- vs. HC; ↑ Parasutterella in T2D+ vs. HC; ↑ Blautia and Eubacterium hallii group in T2D−vs. HC
[26]n = 100; HC n = 50; T2D n = 50↑ Lactobacillus *, ↓ Clostridium leptum and Clostridium coccoides in T2D
[18]n = 36; HC n = 18; T2D n = 18↓ Faecalibacterium prausnitzii in T2D
[19]n = 36; HC n = 18; T2D n = 18↑ Lactobacillus *, ↓ Bifidobacterium in T2D
[68]n = 239; HC n = 54; HT n = 97; HL n = 96; T2D n = 162↑ Bifidobacterium in HL, T2D, RISK1, and RISK2; ↑ Collinsella in HT, HL, T2D, RISK2, and RISK3; ↑ Escherichia in RISK3; ↓ Alistipes in HL
[27]n = 98; HC n = 47; T1D n = 51↑ Blautia, Anaerostipes, Eubacterium hallii group, Dorea, Collinsella, and Klebsiella, ↓ Parabacteroides and Flavonifractor in T1D
[69]n = 29; HC n = 8; T1D at onset n = 8; T1D two years treatment n = 13↑ Bacteroides, ↓ Prevotella, Megamonas, and Acidaminococcus in T1D at onset
[70]n = 47; HC n = 7; T1D n = 22; T2D n = 18↑ Pseudomonas and Prevotella in T1D and T2D vs. HC
[20]n = 110; HC n = 40; T1D n = 21; T2D n = 49↑ Escherichia, Prevotella, and Lactobacillus *, ↓ Bacteroides, Roseburia, and Bifidobacterium in T1D and T2D; ↓ Faecalibacterium in T1D vs. T2D and HC
[21]n = 43; HC n = 13; T1D n = 15; MODY2 n = 15↑ Bacteroides, Ruminococcus, Blautia, Veillonella, Streptococcus, Sutterella, and Enterobacter, ↓ Bifidobacterium in T1D; ↑ Prevotella ↓ Lachnospira, Roseburia, Anaerostipes, and Faecalibacterium in T1D and MODY2
[71]n = 60; HC n = 30; Metsyn patients n = 30↑ Clostridium leptum, Clostridium coccoides group, and Turicibacter sp., ↓ Butyricicoccus sp., Faecalibacterium prausnitzii, and Akkermansia muciniphila in Mets
[72]n = 655; MZ n = 306; DZ n = 74, Nontwin n = 275↑ Lactobacillus *, Sutterella, Dorea, and Methanobrevibacter, ↓ Parabacteroides, Bifidobacterium, Odoribacter, Akkermansia, and Paraprevotella in Mets
[13]n = 20; No Mets + NGT n = 4; No Mets + IFG n = 3; No Mets + IFG + IGT n = 1; Mets + IFG n = 4; Mets + IFG + IGT n = 4; Mets + T2D n = 4↑ Ruminococcus, Dorea, Blautia, and Oscillospira in OB, Mets, IFG, IFG + IGT, and T2D
[28]n = 41; HC n = 20; UC n = 21↓ Ruminococcus and Faecalibacterium prausnitzii in UC
[29]n = 20; HC n = 10; UC n = 10↑ Escherichia-Shigella, Peptostreptococcus, Bacillus, and Veillonella, ↓ Akkermansia, Faecalibacterium, and Bifdobacterium in UC
[30]n = 42; HC n = 14; UC n = 28↑ Streptococcus, Escherichia-Shigella, Romboutsia, Clostridium sensu stricto, Enterococcus, and Citrobacter, ↓ Faecalibacterium, Agathobacter, Dorea, Ruminococcus, Prevotella, Alistipes, Parabacteroides, and Butyricicoccus in UC
[73]n = 53; HC n = 23; UC n = 12; PSC n = 11; PSC + UC n = 7↑ Bifidobacterium in UC
[31]n = 24; HC n = 12; CD n = 6; UC n = 6↑ Clostridium ramosum, Escherichia coli, Fusobacterium nucleatum, and Ruminococcus gnavus, ↓ Eubacterium rectale, and Faecalibacterium prausnitzii in UC
[32]n = 58; HC n = 29; UC n = 22; CD n = 7↓ Bacteroides, Faecalibacterium prausnitzii, Prevotella spp., and Methanobrevibacterium spp. in IBD
[33]n = 42; HC n = 13; CD n = 15; UC n = 14↑ Abiotrophia, Pseudoramibacter, Eubacterium, and Escherichia, ↓ Butyricicoccus, Mitsuokella, Haemophilus, and Victivallis in CD; ↑ Granulicatella, Peptostreptococcus, Schwartzia, Capnocytophaga, Escherichia, Janthinobacterium, Campylobacter, Actinomyces, Eggerthella, and Corynebacterium, ↓ Holdemania, Lachnobacterium, Megamonas, Mitsuokella, Alistipes, Butyricimonas, Prevotella, Desulfovibrio, Oxalobacter, Pyramidobacter, and Victivallis in UC; ↑ Pseudoramibacter Eubacterium, Desulfovibrio, and Slackia, ↓ Butyricicoccus, Moryella, Staphylococcus, Capnocytophaga, Haemophilus, Janthinobacterium, Cardiobacterium, Lautropia, Lupinus, Shewanella, and Corynebacterium in CD/UC
[34]n = 155; Non-IBD n = 34; CD n = 68; UC n = 53↑ Unclassified Roseburia species in CD and UC; ↑ Bifidobacterium breve and Clostridium symbiosum in UC; ↑ Blautia producta, Lactobacillus gasseri, Enterococcus faecium, Clostridium clostridioforme, Ruminococcus gnavus, and Escherichia coli in CD
[74]n = 1087; HC n = 290; IBD n = 512; CRC n = 285↑ Bacteroides in IBD
[35]n = 68; HC n = 48; IBD n = 20↑Bifidobacterium, Ruminococcus gnavus group, Streptococcus, and Blautia, ↓ Faecalibacterium, Subdoligranulum, Parabacteroides, and Paraprevotella in IBD
[36]n = 30; HC n = 8; DD n = 4; IBS n = 3; UC n = 5; CD n = 10↑ Dialister spp. And Faecalibacterium prausnitzii in IBS; ↑ Bacteroides fragilis, Dialister spp., and Roseburia spp. ↓ Clostridium difficile in UC vs. HC; ↑ Parabacteroides distasonis ↓ Faecalibacterium prausnitzii, and Bacteroides fragilis in CD
[37]n = 69; HC n = 40; Non-PN SBS n = 5; SBS I n = 10; SBS II n = 14↑ Lactobacillus * and Klebsiella, ↓ Coprococcus, Faecalibacterium, Lachnospira, and Ruminococcus in SBS patients; ↓ Blautia, Bacteroides, Odoribacter, Oscillospira, Prevotella, Roseburia, and Sutterella in SBS I and SBS II; ↑ Streptococcus and Staphylococcus in SBS I
[75]n = 16 NAFLD↑ Prevotella copri and Prevotella stercorea in NAFLD
[76]n = 68; HC n = 36; NAFLD n = 32↑ Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae, ↓ Akkermansia muciniphila, Alistipes putredinis, Bacteroides uniformis, Bacteroides fragilis, Oscillibacter sp., Ruminococcus bromii, Eubacterium ventriosum, and Gemmiger formicilis in NAFLD
[77]n = 874; Non-NAFLD n = 669; NAFLD n = 205↓ Faecalibacterium and Bacteroides in NAFLD
[78]n = 766; Control n = 453; Developed NAFLD n = 40; Regressed NAFLD n = 35; Persistent NAFLD n = 238↓ Oscillospira, Odoribacter, and Coprococcus in persistent NAFLD vs. Control; ↓ Coprococcus eutactus in regressed NAFLD and persistent NAFLD vs. Control
[79]n = 67; HC n = 37; NAFLD n = 30↑ Porphyromonas, Succinivibrio, Clostridium, Blautia, Dorea, Peptococcus, Mitsuokella, and Slackia, ↓ Odoribacter, Proteus, and Coprococcus in NAFLD
[80]n = 47; HC n = 22; NAFLD n = 25↑ Escherichia-Shigella, Blautia, Clostridium XVIII, and Streptococcus, ↓ Prevotella and Faecalibacterium in NAFLD
[81]n = 202; no-NAFLD n = 31; NAFLD n = 171↑ Citrobacter, ↓ Coprococcus and Lachnospira in significant fibrosis
[82]n = 126; no-NAFLD n = 83; NAFLD n = 43↓ Coprococcus, Pseudobutyrivibrio, Moryella, Roseburia, Anaerosporobacter, Anaerotruncus, Ruminococcus, Lactobacillus * in NAFLD
[83]n = 75; HC n = 25; NAFLD n = 25; NASH n = 25↑ Bacteroides and Prevotella, ↓ Faecalibacterium in NAFLD and NASH
[84]n = 86; Mild/moderate NAFLD n = 72; Fibrosis n = 14↑ Eubacterium rectale in mild/moderate NAFLD; ↑ Bacteroides vulgatus and Escherichia coli, ↓ Ruminococcus obeum, and Eubacterium rectale in fibrosis
[85]n = 24; HC n = 8; NASH n = 16↑ Phascolarctobacterium in NASH
[86]n = 67; HC n = 28; NASH n = 24; SS n = 15↓ Ruminococcus, Faecalibacterium prausnitzii, and Coprococcus in NAFLD and SS vs. HC
[87]n = 50; HC n = 17; NASH n = 22; SS n = 11↓ Clostridium coccoides in NASH
[88]n = 60; Non significant fibrosis n = 35; Significant fibrosis n = 25↑ Bacteroides and Lactobacillus *, ↓ Bifidobacterium in significant fibrosis
[89]n = 40; NT n = 15; HT n = 25↑ Rothia ↓ Faecalicoccus, Morganella, Acetohalobium, and Phaeodactylibacter in HT
[90]n = 70; NT n = 47; HT n = 23↑ Acidaminococcus, Eubacterium, and Alistipes in HT
[91]n = 80; NT n = 32; HT n = 48↑ Ligilactobacillus salivarius, Bacteroides plebeius, and Eggerthella, ↓ Roseburia faecis, Faecalibacterium prausnitzii, Parabacteroides distasonis, Unclassified Fusobacterium, and Coprobacillus in HT
[92]n = 120; HC n = 60; HT n = 60↑ Klebsiella, Clostridium, Streptococcus, Parabacteroides, Eggerthella, and Salmonella, ↓ Faecalibacterium, and Roseburia in HT
[93]n = 196; HC n = 41; pHT n = 56; HT n = 99↑ Prevotella and Klebsiella in pHT or HT; ↑ Porphyromonas and Actinomyces in HT; ↓ Faecalibacterium, Oscillibacter, Roseburia, Subdoligranulum, Blautia, Bifidobacterium, Coprococcus, Butyrivibrio, Eggerthella, Streptococcus, and Akkermansia in pHT and HT
[94]n = 900; HC n = 300; HT n = 300; CAD n = 300↑ Escherichia in HT
[95]n = 235; HC n = 42; NH n = 63; AH n = 104; HLD n = 26↑ Blautia, Bacteroides, and Faecalibacterium in NH; ↑ Bacteroides and Faecalibacterium in HLD and HC
[96]n = 502; HC n = 100; ACS n = 402↑ Escherichia coli and Streptococcus, ↓ Lactobacillus * in ACS
[97]n = 64; HC n = 32; CAS n = 32↑ Acidaminococcus, Christensenella, and Lactobacillus *, ↓ Anaerostipes, Fusobacterium, Gemella, Parvimonas, Romboutsia, and Clostridium XVIII/XlVa/XlVb in CAS
[98]n = 345; No SCA n = 201; SCA n = 144↑ Escherichia and Oscillospira in SCA
[99]Sweden cohort n = 25; Control 1 n = 13; Atherosclerosis 1 n = 12; China cohort n = 385; Control 2 n = 171; Atherosclerosis 2 n = 214↑ Bifidobacterium adolescentis, Collinsella aerofaciens, Blautia hydrogenotrophica, and Anaerotruncus colihominis in atherosclerosis 1; ↑ Bacteroides fragilis, Streptococcus salivarius, Clostridium nexile, Ruminococcus gnavus, Ruminococcus torques, coli, Klebsiella pneumoniae, and Akkermansia muciniphila in atherosclerosis 2
[100]n = 106; Control n = 53; CAD n = 53↑ Porphyromonas, Prevotella, Agathobacter, Ruminococcus gnavus, Catenibacterium, and Succiniclasticum, ↓ Anaerosporobacter, Coprococcus, Eisenbergiella, Fusocatenibacter, Eubacterium hallii, Ruminococcus gauvreauii, Fournierella, and Veillonella in CAD
[101]n = 201; HC n = 40; CAD n = 161↑ Actinomyces, Haemophilus, Granulicatella, Weissella, Veillonella, Streptococcus, Klebsiella, Rothia, Enterococcus (CAG17); ↓ Faecalibacterium, Roseburia, Oscilibacter (CAG4); Ruminococcus 2, Dorea, Blautia, Clostridium XVIII (CAG14); Anaerostipes, Blautia, Lactobacillus *, Fusocatenibacter, Clostridium XIVa, Gemella, Bifidobacterium, Saccharibacteria genera incertae sedis (CAG15); Roseburia, Clostridium XIVb, Parasutterella, Butyricicoccus (CAG16) in CAD
[102]n = 405; HC n = 187; ACVD n = 218↑ Escherichia coli, Klebsiella spp., Enterobacter aerogenes, Streptococcus spp., Ligilactobacillus salivarius, Solobacterium moorei, Atopobium parvulum, Ruminococcus gnavus, and Eggerthella lenta, ↓ Roseburia intestinalis, Faecalibacterium prausnitzii, Bacteroides spp., Prevotella copri, and Alistipes shahii in ACVD
AbOB: abdominal obesity; ACS: acute coronary syndrome; ACVD: atherosclerotic cardiovascular disease; AH: hypertensive patients undergoing anti-hypertensive treatment; CAD: coronary artery disease; CAG: co-abundance group; CAS: carotid atherosclerosis; CD: Crohn’s disease; CN: cognitive normal group; CRC: colorectal cancer; DD: diverticular disease; DR: diabetic retinopathy; Dys: dyslipidemia; DZ: dizygotic twin pairs; HC: healthy control; HL: hyperlipidemia; HLD: normal blood pressure but with hyperlipidemia; HT: hypertension; IBD: inflammatory bowel disease; IBS: irritable bowel syndrome; IFG: impaired fasting glycemia; IGT: impaired glucose tolerance; KnownT2D: diabetics on antidiabetic treatment; LN: lean; Mets: metabolic syndrome; MODY2: maturity-onset diabetes of the young 2; MZ: monozygotic twin pairs; NAFLD: non-alcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; NewT2D: newly diagnosed diabetic; NGT: normal glucose tolerance; NH: hypertensive patients with treatment-naive hypertension; Non-PN SBS: parenteral nutrition-independent short bowel syndrome; NT: normotension; OB: obese; OW: overweight; pHT: prehypertension; PSC: primary sclerosing cholangitis; pT2D: prediabetic; RISK1: patients with only one disease; RISK2: patients with two diseases; RISK3: patients with three diseases; SBS I: parenteral nutrition-dependent short bowel syndrome I; SBS II: parenteral nutrition-dependent short bowel syndrome II; SCA: subclinical carotid atherosclerosis; SS: simple steatosis; T1D: type 1 diabetes; T2D: type 2 diabetes; T2D+: type 2 diabetes with chronic complications; T2D-: type 2 diabetes without chronic complications; T2DCI: type 2 diabetes cognitive impairment group; UC: ulcerative colitis. * Lactobacillus includes species from Lactobacillaceae family [22]. ↑ Taxa increasement and ↓ Taxa decreasement.
Table
Table 2. Disease/health-related metabolites and chemical classification.
Table 2. Disease/health-related metabolites and chemical classification.
Health-Related MetabolitesDisease-Related Metabolites
Fatty Acid Pathways—Metabolites and conjugates
10-Heptadecenoate (17:1n7) (+)-Cucurbic acid
2-Hydroxyhexadecanoate 12,13-Dihydroxy-11-methoxy-9-octadecenoic acid
Acetate 17-Oxo-octadecanoic acid
Azelaic acid 2-Hydroxyadipate
Caproic acid2-Methyl-tridecanedioic acid
Caprylic acid3-Keto stearic acid
Isovalerate8,11,14-Eicosatrienoic acid
Undecanedionate8Z-Decen-4,6-diynoic acid
9,10-Dichloro-octadecanoic acid
Adrenic acid
Arachidonic acid
Diamino-pimelic acid
Dihomo-linolenate (20:3n3 or n6)
Docosahexaenoic acid
Docosanedioic acid
Eicosatrienoic acid
Linolenic acid
Amino Acid Pathways—Metabolites and derivatives
GlycylvalineAsymmetric dimethylarginine (ADMA)
IsoleucineCarnosine
N6,N6,N6-TrimethyllysineCinnamoylglycine
N-AcetylalanineCitrulline
S-Carboxymethyl-L-cysteineƔ-Glutamylglutamine
ValineGlycine
Homocitrulline
Homocysteine
L-Lysine
N6-Carboxymethyllysine
Nɑ-Acetyl-L-arginine
Propionylglutamine
Biliary Acid Pathways—Metabolites and derivatives
Chenodeoxyglycocholate12-Dehydrocholic acid
Glycoursodeoxycholic acid3-Dehydrocholic acid
3β-Cholic acid
6,7-Diketolithocholic acid
6-Keto-Lithocholic acid
7,12-Diketolithocholic acid
7-Dehydrocholic acid
7-Ketolithocholic acid
Allocholic acid
Chenodeoxycholic acid
Chenodeoxycholic acid-3Gln
Cholate sulfate
Dehydrocholic acid
Glycochenodeoxycholic acid
Glycodeoxycholic acid
Glycolithocholic acid
Hyodeoxycholic acid
Lithocholic acid
Murocholic acid
Nordeoxycholic acid
Taurocholic acid
Taurohyocholic acid
Taurolithocholic acid
Tauroursodeoxycholic acid
αMuricholic acid
βDeoxycholic acid
βMuricholic acid
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MDPI and ACS Style

Torres-Sánchez, A.; Ruiz-Rodríguez, A.; Ortiz, P.; Aguilera, M. Key Stratification of Microbiota Taxa and Metabolites in the Host Metabolic Health–Disease Balance. Int. J. Mol. Sci. 2023, 24, 4519. https://doi.org/10.3390/ijms24054519

AMA Style

Torres-Sánchez A, Ruiz-Rodríguez A, Ortiz P, Aguilera M. Key Stratification of Microbiota Taxa and Metabolites in the Host Metabolic Health–Disease Balance. International Journal of Molecular Sciences. 2023; 24(5):4519. https://doi.org/10.3390/ijms24054519

Chicago/Turabian Style

Torres-Sánchez, Alfonso, Alicia Ruiz-Rodríguez, Pilar Ortiz, and Margarita Aguilera. 2023. "Key Stratification of Microbiota Taxa and Metabolites in the Host Metabolic Health–Disease Balance" International Journal of Molecular Sciences 24, no. 5: 4519. https://doi.org/10.3390/ijms24054519

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