Probiotics and Metabolic Syndrome: A Bibliometric Analysis and Overview of Dietary Interventions

1. Introduction

The composition of the microbiota is highly variable among individuals and can fluctuate significantly within an individual due to various environmental factors such as antibiotic use, lifestyle, hygiene, and diet, all of which have a direct impact on the host’s health [1].

Probiotics and prebiotics are the primary modulators of the intestinal microbiota, and together, they are known as synbiotics [2]. This synergy has been shown to benefit the immune system and gut microbiota, offering potential strategies for preventing and treating dietary component-induced intestinal inflammation and inflammatory diseases [3].

Prebiotics, as defined by the Food and Agriculture Organization of the United Nations (FAO), are non-digestible food ingredients that reach the colon in an unaltered form and are fermented by saccharolytic bacteria [2]. These compounds predominantly include complex carbohydrates such as resistant starch, inulin, cellulose, pectin, hemicellulose, and gum, as well as substances like lactulose, fructooligosaccharides, and galactooligosaccharides [4]. Upon fermentation, prebiotics enhance the size and activity of beneficial bacteria in the colon, promoting the growth of a healthy gut microbiota [5].

The effects of prebiotics on the gut microbiota and their potential health benefits have been extensively studied. Research has shown that prebiotics can modulate the intestinal microbiota and benefit the host’s health [6]. Furthermore, prebiotics have been associated with producing short-chain fatty acids with immunomodulatory properties, which can contribute to regulating gut permeability and reducing inflammation [7]. Additionally, prebiotics have been investigated for their potential to improve metabolic health, with studies revealing their influence on the production of glucagon-like peptide 1 and peptide YY and their ability to decrease ghrelin levels [8].

Probiotics defined by FAO/OMS are “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” [9]. These microorganisms primarily consist of lactic acid bacteria and yeasts, and their beneficial effects are attributed to various mechanisms, including the generation of short-chain fatty acids by specific beneficial bacteria, which play a crucial role in regulating glucose homeostasis [10].

The effects of probiotic supplementation on metabolic issues such as hyperglycemia, hypertension, and hyperlipidemia have been extensively studied. Clinical studies, systematic reviews, meta-analyses, and umbrella reviews have been conducted to assess the quality of the information in these reviews, providing a comprehensive understanding of the impact of probiotics on metabolic health [11, 12, 13, 14, 15, 16, 17]. These studies have investigated the potential of probiotics to ameliorate various metabolic parameters, including glucose metabolism, lipid profiles, and insulin resistance, among others. While some studies have reported positive effects of probiotics on metabolic parameters, others have failed to find significant benefits, leading to a lack of consensus in the field [18, 19, 20].

The variability in the results may be attributed to differences in study designs, participant characteristics, probiotic strains, and dosages used, highlighting the complexity of evaluating the overall impact of probiotics on metabolic health. Despite the challenges in reaching a consensus, evidence from several randomized controlled trials and meta-analyses indicates that probiotics can have a beneficial effect on aspects of metabolic syndrome, including blood pressure, glucose metabolism, and blood lipid profiles, as well as improving inflammatory biomarkers [21, 22, 23].

This book chapter aims to analyze the clinical evidence surrounding the use of probiotics for metabolic syndrome. It includes a bibliometric analysis of clinical studies, systematic reviews, meta-analyses, and umbrella reviews, thoroughly evaluating the existing literature on probiotics and their impact on metabolic syndrome. Additionally, the chapter reviews the principal dietary interventions for metabolic syndrome and their reported effects on the microbiota, exposing the potential mechanisms underlying the observed outcomes.

2. Bibliometric and authors’ keywords analysis

Data gathering relied on scientific articles on Web of Science© (WoS) published and indexed. On January 23, 2024, a search was conducted on WoS using the “advanced search” section, employing the following logical operation: (TI = (‘probiotic*’ AND ‘metabolic syndrome’) OR AB = (‘probiotic*’ AND ‘metabolic syndrome’)) AND (TI = (‘systematic review’ OR ‘meta-analysis’ OR ‘umbrella review’ OR ‘controlled trial*’ OR ‘overview’) OR AB = (‘systematic review’ OR ‘meta-analysis’ OR ‘umbrella review’ OR ‘controlled trial*’ OR ‘overview’)) NOT KP = (‘probiotic*’ AND ‘metabolic syndrome’ AND ‘systematic review’ AND ‘meta-analysis’ AND ‘umbrella review’ AND ‘controlled trial*’ AND ‘overview’). This function allowed search articles containing the terms probiotic or probiotics (“probiotic*”) and metabolic syndrome in the title (TI) and abstract (AB). Also, it included at least one of the terms “systematic review”, “meta-analysis”, or “umbrella review” rather than relying on the keywords-plus associated with each document (NOT KP). The logical operators combined the conditions. The search excluded Book Chapters, Proceeding Papers, Early Access Articles, and Retracted Publications.

An analysis was conducted on publications spanning from 2014 to 2023, encompassing all records available in WoS. This examination yielded a total of 92 documents. Data were exported as BibTeX (savedrecs.bib) and Research Information Systems (savedrecs.ris) files using the “Record Content: Full Record” option. Analysis was performed with the software VOSviewer version 1.6.20 and with software R-package bibliometrix version 4.1.4 [24] for the scientific mapping analysis, using R version 4.1.2 as the programming language (2021-11-01) [25], and R-studio version 2023.12.0 as integrated development environment [26]. The analysis was conducted utilizing the biblioshiny() function.

The outcome of data analysis is presented in Table 1, which comprehensively analyzes research data spanning the timespan study (2014–2023). It highlights significant trends in document content, authorship, collaboration, and document types. Notably, the research landscape witnessed a robust annual growth rate of 31.8%, with 69 sources contributing to 92 documents. The absence of single-authored documents suggests a prevalent culture of collaboration among 475 authors. The 23.91% international co-authorships emphasize the study’s global dimension. Document content is rich, featuring 413 Keywords Plus (ID) and 275 Author’s Keywords (DE). The average document age of 3.52 years indicates the timeliness of the research, while the citation rate of 26.36 reflects its impact. Categorically, the documents include 51 articles and 41 reviews, showcasing a balanced mix of original research and critical assessments. The analysis provided valuable insights into research activities’ dynamic and collaborative nature during the specified period.

Additionally, Figure 1 serves as a multifaceted tool for understanding the intricacies of scientific production from 2014 to 2023. Each sub-figure provides a unique perspective, collectively contributing to a nuanced and comprehensive portrayal of the research landscape during the specified timespan. Researchers and stakeholders can leverage this information to identify trends, assess global contributions, and gain valuable insights into the evolving dynamics of scientific output.

Figure 1a outlines the annual scientific production from 2014 to 2023, revealing distinctive trends. Initial years (2014–2015) show sparse output, with two articles annually, followed by a notable absence in 2016. From 2017 to 2020, a gradual increase occurred, peaking at 15 articles in 2021 but experiencing fluctuations in 2020 and 2022. Notably, 2023 sees a significant surge with 24 articles, suggesting a particularly productive year. The data underscores dynamic shifts in research output, potentially influenced by evolving research priorities, external events, and varying funding scenarios. This analysis provides valuable insights for researchers and stakeholders, identifying influential periods and informing strategic planning for future research endeavors.

Figure 1b presents a frequency distribution in research output across different regions, indicating the number of occurrences for each area. Iran leads with 171 instances, followed by Spain with 72 and China with 64. The United States, often a prominent contributor to scientific research, ranks fourth with 56 occurrences. Germany, India, Italy, Australia, Canada, and Brazil follow in descending order, contributing 34 to 14 instances each. This distribution suggests a varied global landscape of research contributions, with Iran demonstrating a notable presence in the dataset. Figure 1b provides valuable insights into regional research activity, aiding in understanding the geographical distribution of scientific output in the analyzed context.

Figure 1c provides an overview of the leading journals where articles were published, detailing the frequency of publications for each source. “Nutrients” emerges as the primary source with six articles, suggesting a significant focus on this avenue within the analyzed dataset. Following closely are “Gut Microbes”, “Journal of Functional Foods”, and “Trials”, each with four articles indicating substantial research output from these platforms. Several other journals, such as “Clinical Nutrition”, “British Journal of Nutrition”, and “Critical Reviews in Food Science and Nutrition”, contribute two to three articles each. Researchers and stakeholders can leverage this information to identify critical platforms driving discussions and insights within the realm of nutrition.

Figure 1d highlights the local impact of various sources in the field, as measured by their h-index. “Gut Microbes” leads with an h-index of 4, signifying its substantial influence and broad citation impact within the analyzed dataset. Following closely are “Journal of Functional Foods”, “Nutrients”, and “Trials”, each with an h-index of 3, indicating significant recognition and impact in the local scholarly community. Several other sources, including “British Journal of Nutrition”, “Clinical Nutrition”, “Critical Reviews in Food Science and Nutrition”, “European Journal of Nutrition”, “Frontiers in Nutrition”, and “Nutrition Metabolism and Cardiovascular Diseases”, maintain an h-index of 2, reflecting a notable but slightly lower local impact. This metric provides valuable insights into the scholarly influence and recognition of these sources within the specific domain, aiding researchers and stakeholders in gauging the significance and reach of these platforms in nutrition.

Finally, Figure 1e categorizes the research articles into various Web of Science categories, indicating the record count and the corresponding percentage of 92 articles. “Nutrition Dietetics” is the predominant category, encompassing 39.13% of the articles, showcasing a significant focus on nutritional studies within the dataset. Other prominent categories include “Endocrinology Metabolism”, “Food Science Technology”, and “Pharmacology Pharmacy”, each contributing around 12% of the total articles. “Medicine General Internal” follows closely, representing 9.78% of the dataset. The distribution across categories reflects diverse research interests, with microbiology, gastroenterology, and experimental medicine also making notable contributions. This categorization provides a comprehensive overview of the research landscape, highlighting the multifaceted nature of studies within nutrition and related disciplines. Researchers and stakeholders can leverage this information to understand the distribution of topics and the emphasis on specific areas within the analyzed dataset.

Regarding the analysis of authors’ keywords, Figure 2 provides a comprehensive exploration that researchers can utilize to identify patterns, recognize emerging trends, and acquire valuable insights into the thematic landscape of research contributions from 2014 to 2023. The integration of these visualization techniques improves the interpretability of the dataset, enabling a more nuanced understanding of the relationships and dynamics embedded within the usage of authors’ keywords. Figure 2a shows a word cloud generated from the top 50 words with the highest frequency, visually representing vital thematic elements in the dataset. “Gut microbiota”, “double-blind”, and “insulin resistance” emerge as central terms, each with significant frequencies of 28, 25, and 18, respectively. These terms suggest a strong emphasis on topics related to gut microbiota, experimental design (double-blind studies), and metabolic health (insulin resistance) within the corpus. Other prevalent terms, including “meta-analysis”, “supplementation”, “inflammation”, “obesity”, “health”, “lipid profile”, and “probiotics”, contribute to a rich thematic landscape. The prominence of these terms in the word cloud indicates their recurrent presence in the dataset, offering researchers valuable insights into the focal points of research contributions and potential areas of emphasis within the analyzed period.

Figure 2b–d depict the scientometric mapping conducted using VOSviewer v. 1.6.18 software, following the first two steps reported in a prior study [27]. The next steps followed the methodology reported in a previous investigation (3). The counting method was configured to utilize full counting. (4) A threshold of five occurrences was established to ensure the inclusion of numerous concepts in the map. Of the 2208 terms, 72 meet the threshold. (5) The software calculated a relevance score for each of the 72 terms [28]. Using this score, the terms deemed most relevant were chosen. The default setting, which comprised 60% of the most relevant terms, was selected, resulting in 43 terms. However, the terms “mg/dL”, “baseline”, “individual”, “type”, “patient”, “participant”, and “week” were excluded because they are common terms related to clinical studies, leaving a final count of 36 terms. Although certain words may appear in different nodes, they refer to the same term. That is the case of “polycystic ovary syndrome, pco and pcos” “diabetes and diabete” and “synbiotic supplementation and symbiotic”. (6) The maps were created. The normalization method chosen was the association strength. The cluster size was maintained at least one term per cluster, adhering to the default setting. The resolution parameter was set to one (default value). Finally, the maps were exported as PNG image files.

Figure 2b displays 36 items, 3 clusters, and 471 links, with a total link strength of 4944. The sizes of the letters and circles correspond to the frequency of occurrences, highlighting significant points within the chosen research domain. The proximity of keywords in the visualization reflects their connectedness through occurrence links, with closer keywords indicating a stronger relationship. The distances between keywords can serve as indicators of knowledge gaps within specific areas.

The dataset analysis with VOSviewer has revealed three distinct clusters, each highlighting specific areas of health research. The first cluster explores cardiovascular and metabolic health, emphasizing factors like blood pressure and weight loss. The second cluster focuses on evidence-based research into metabolic disorders such as diabetes and obesity. The third cluster delves into women’s health and gut microbiota modulation, particularly hormonal aspects. Collectively, these clusters offer valuable insights into diverse aspects of metabolic health research:

1. The first cluster with 15 items, “Cardiovascular and Metabolic Health”, red (R: 214, G: 39, B: 40), comprises terms closely associated with cardiovascular health, metabolic disorders, and related interventions. The presence of terms such as “blood pressure”, “lipid profile”, “waist circumference”, and “weight loss” suggests a focus on factors related to cardiovascular risk assessment and management. Additionally, “vitamin D” and “yogurt” indicate potential dietary and lifestyle interventions that may influence cardiovascular and metabolic health outcomes. Including terms like “double-blind” and “placebo” underscores the importance of rigorous experimental design and control groups in studies investigating interventions targeting cardiovascular and metabolic parameters. Overall, this cluster reflects a comprehensive exploration of factors and interventions relevant to cardiovascular and metabolic health, highlighting the interdisciplinary nature of research in this domain.

2. The second cluster with 13 items, “Metabolic Disorders and Evidence-Based Research”, green (R: 44, G: 160, B:44), encompasses terms indicative of in-depth investigations into metabolic disorders, particularly diabetes, obesity, and non-alcoholic fatty liver disease (NAFLD). The inclusion of terms like “evidence”, “mechanisms”, and “systematic review” suggests a strong emphasis on rigorous and evidence-based research methodologies within the cluster. The term “overview” implies a holistic approach, possibly involving comprehensive reviews or analyses of the current state of knowledge in the field of metabolic disorders. The presence of terms related to prevention underscores the proactive exploration of strategies to mitigate or manage metabolic disorders. Thus, Cluster 2 signifies a research focus on metabolic conditions, supported by evidence-based approaches, with an overarching goal of understanding mechanisms and prevention and offering comprehensive overviews through systematic reviews and articles.

3. The third cluster with 8 items, “Women’s Health and Gut Microbiota Modulation”, blue (R: 31, G: 119, B: 180), centers around terms related to women’s health and the modulation of gut microbiota. The inclusion of terms like “PCOS” (Polycystic Ovary Syndrome), “woman”, and “insulin resistance” (indicated by “homa ir”) points toward a specific focus on women’s health concerns, particularly those related to hormonal and reproductive aspects. Additionally, terms such as “dysbiosis”, “prebiotic”, “probiotic”, and “synbiotic” highlight an interest in understanding and potentially modulating the gut microbiota as it relates to women’s health. This cluster suggests a nuanced exploration of the interplay between gut health, hormonal imbalances, and conditions like PCOS, indicating a comprehensive investigation into the potential role of microbiota modulation in women’s health and associated disorders.

On the other hand, Figure 2c displays an “overlay visualization” graph, employing a score range from 2020 to 2021.5, with the color scheme set to Viridis default. This visualization effectively represents the distribution and relationships among elements in the dataset over time. The “overlay visualization” offers a dynamic and detailed representation of data evolution, enabling a quick and precise interpretation of variations from 2020 to 2021.5.

Finally, Figure 2d displays the “density visualization”, representing item density within the dataset. The “item density” option from VOSviewer software shows the graph’s spatial concentration or distribution of elements. In a density visualization, areas with higher color intensity indicate a greater density of components, potentially suggesting the presence of denser thematic clusters or groups in those regions. Conversely, lighter-colored areas denote lower density, indicating possibly less populated zones regarding dataset elements, aiding in identifying patterns, clusters, and areas of greater relevance within the analyzed dataset.

3. Use of probiotics in metabolic syndrome

The use of probiotics in managing metabolic syndrome and its associated conditions, such as prediabetes and diabetes, has gained significant attention in recent research. Probiotics have been shown to have beneficial effects on various aspects of metabolic syndrome, including low-grade inflammation, immunity, oxidative stress, and lipid concentrations, as well as glycemic control [29, 30, 31]. Many studies have demonstrated the positive impact of specific bacterial strains on glycemic control in animal models, indicating the potential of probiotics in addressing metabolic disorders [29, 30].

Furthermore, probiotics have been associated with improvements in oxidative stress and inflammation markers, supporting their potential cardio-protective effects [31]. The regulation of probiotics on the immune system has also been highlighted as a method to prevent common diseases, indicating a broader impact beyond metabolic syndrome [32, 33]. Additionally, the modulation of lipid metabolism and lipid profiles, including improving HDL-C concentration, has been documented in individuals with type 2 diabetes [30, 34].

The potential of probiotics extends to other metabolic disorders, as evidenced by their ability to reduce homocysteine concentration in obese women, indicating a broader application in addressing metabolic abnormalities [35]. Additionally, the impact of glycemic control on various aspects of metabolic disorders, including lipid metabolism, immune response, and susceptibility to infections, underscores the interconnected nature of these conditions [36, 37].

While the evidence supporting the beneficial effects of probiotics on metabolic syndrome is substantial, it is essential to consider factors such as bacterial strains, dosage, and study heterogeneity when evaluating the effectiveness of probiotics in preventing or treating metabolic disorders [30, 38]. In a systematic review of randomized controlled trials that employed probiotics, prebiotics, or synbiotics for pre-diabetes treatment, the partial demonstration of benefits in modulating gut microbiota abundance has been observed. The authors emphasized that there is insufficient evidence to support the significant benefits of biotics in glucose metabolism, lipid metabolism, and body composition. Additionally, they highlighted the impact of small sample sizes and various study designs for comparing studies [39].

Recently, an umbrella meta-analysis was carried out to evaluate the effect on glycemia of the information available in databases such as Scopus, Embase, Pubmed, Web of Science, and Google Scholar on clinical studies on probiotic supplementation. The PRISMA methodology was followed, and a record was generated on the PROSPERO platform (Registration code: CRD42021286290). This work excluded observational papers, case reports, in vitro, ex vivo, in vivo, and quasi-experimental, controlled clinical trials, and articles in a language other than English. The AMSTAR methodology was used to evaluate the quality of work. The credibility of the meta-analyses was assessed using GRADE (Grading of Recommendations, Assessment, and Evaluation). Of 693 articles, only 48 met the inclusion criteria for the analysis and were published from 2013 to 2021. They concluded that probiotics benefit fasting plasma glucose, HbA1c, HOMA-IR, and insulin levels. A probiotic supplementation period of less than 8 weeks at moderate dosages (10⁸ or 10⁹ CFUs) was a more effective approach in improving these parameters [40].

In a PRISMA methodology umbrella meta-analysis (PROSPERO registration number = CRD42022304378), the effects of synbiotic supplementation on insulin, fasting blood sugar, and HOMA-IR were evaluated. For the selection and quality assessment of the included studies, AMSTAR2 checklist and GRADE were used. Of the total 156 articles, only 13 were selected. The study concluded that synbiotic supplementation can improve the glycemic indices studied and can be recommended as an adjunctive anti-hyperglycemic agent, particularly in diabetic and PCOS patients [41].

The therapeutic potential of probiotics in managing metabolic syndrome is supported by evidence of their positive impact on inflammation, immunity, oxidative stress, lipid concentrations, and glycemic control [42]. Probiotics have been shown to reduce systemic inflammation, decrease intestinal endotoxin, and lower insulin resistance and hyperglycemic incidences [43]. Furthermore, probiotics have demonstrated a glucose-lowering effect in participants with type 2 diabetes mellitus [44]. Studies have also indicated that probiotics, particularly Lactobacillus sub-strains, have beneficial effects on diabetes-related blood parameters, although more evidence from human trials is needed to confirm these effects [42]. Additionally, synbiotics have shown efficacy in improving glycemic indices, suggesting their use as a supplementary treatment for conditions like diabetes and polycystic ovary syndrome [39].

However, the efficacy of probiotics is influenced by the specific strains used, the dosage administered, and the variability among studies [43]. Future research should aim to standardize probiotic treatments and further elucidate the mechanisms by which they exert their beneficial effects on metabolic health [42]. It is important to note that the effects of probiotics on glycemic control and metabolic parameters in gestational diabetes mellitus are still being investigated, and further studies are warranted to address the limitations of existing evidence and better inform the management of this ailment [45, 46].

4. Dietary patterns in metabolic syndrome and their impact on the intestinal microbiota

Diet plays an essential role in the management of metabolic syndrome, directly influencing the composition and function of the intestinal microbiota [47]. Numerous dietary interventions have been proposed for patients with metabolic syndrome, emphasizing the importance of increased fiber consumption and analyzing the impact of lipids on the microbiome.

Numerous studies have demonstrated that dietary patterns emphasizing recommendations based on healthy high-fiber foods, such as the Mediterranean regional diet, plant-based diets, DASH diet, among others, are the gold standard for extending life expectancy and reducing the risks of cardiovascular diseases [48, 49, 50, 51, 52, 53]. However, there are other dietary patterns that also have cardiovascular health benefits, such as intermittent fasting patterns. Many of these, as we will discuss later, contribute to or do not contribute to the improvement, balance, and diversity of the intestinal microbiome.

The Mediterranean diet is characterized by high consumption of virgin olive oil, whole grains, nuts, fruits, vegetables, and legumes, moderate consumption of fish, seafood, dairy products, and red wine, as well as a reduction in the consumption of red meats, processed meats, and sugar [52]. Numerous studies consistently demonstrate that the Mediterranean diet, especially when enriched with polyphenols and plant proteins, has a positive impact on reducing the risk of cardiovascular diseases (CVDs) and diabetes. These benefits result in a significant decrease in the incidence of cardiovascular events, improvement in lipoprotein function, and increased antioxidant capacity. However, it is crucial to emphasize that patient adherence to this diet and food choices play a crucial role in obtaining these benefits [54, 55, 56]. Beneficial modulation of the gastrointestinal microbiome and associated metabolomic profile has been found, including an increase in total bacteria, Bifidobacterium/E. coli ratio, lower amounts of E. coli, and the relative ratio of Bacteroidetes/short-chain fatty acids (SCFA) in feces [57]. Another study found that a Mediterranean diet partially restores the population of P. distasonis, B. thetaiotaomicron, F. prausnitzii, B. adolescentis, and B. longum in patients with metabolic syndrome [58] and Eubacteria, Prevotella, Bifidobacteria, and Lactobacilli in healthy subjects [59].

On the other hand, plant-based diets have gained popularity due to their potential health benefits and perceived environmental impact. While the term “plant-based” is sometimes used to refer to omnivorous diets with a relatively small component of animal foods, here it is understood to signify either vegetarian (plant-based plus dairy and/or eggs) or vegan (100% plant-based) diets, both characterized by maximal intake of plant products and the reduction or elimination of animal-derived food consumption. Additionally, vegetarian diets have been demonstrated to reduce body weight, fat mass, as well as blood lipids and glucose in patients with cardiovascular disease [56]. However, plant-based diets of low quality (e.g., refined cereals) have been associated with an increased risk of these pathologies [53]. Therefore, it is understood that achieving health benefits with such diets involves limiting animal-derived foods and making informed choices regarding the selection and quality of plant-based foods in the diet. Regarding the microbiome, conclusive findings are lacking; however, it is known that vegans exhibit higher proportions of Bacteroides thetaiotaomicron, Bacteroides/Prevotella, Klebsiella pneumoniae, Faecalibacterium prausnitzii, Clostridium clostridioforme; and lower proportions of Bilophila wadsworthia, Clostridium cluster XIVa [60]. However, another study found a lower count of Bacteroides and Bifidobacterium species, with no differences between vegans and non-vegans [61]. In another study involving a plant-based dietary intervention, the microbial community structure overcame interindividual differences in microbial gene expression but reverted to baseline values within 3 days [62].

DASH (Dietary Approaches to Stop Hypertension) diets represent a dietary pattern established for hypertension management, with a dietary structure similar to the Mediterranean diet. Given that an estimated 80% of individuals with metabolic syndrome also suffer from hypertension, DASH diets are highly relevant for metabolic syndrome treatment [63]. Rich in fruits and vegetables, skimmed milk, whole grains, and with moderate consumption of nuts and legumes, along with reduced amounts of red meats, fats, refined sugars, and sugary beverages, it results in significant blood pressure reduction compared to an American diet [50]. Subsequent clinical studies further confirmed the antihypertensive effects of this diet, expanding the list of positive effects to include improvements in other cardiovascular risk factors and comorbidities [64]. Likewise, systematic reviews and meta-analyses have demonstrated that the DASH diet significantly reduces body weight, improves lipid profile, blood glucose levels, insulin resistance, inflammatory response, and oxidative stress markers, as well as reduces the incidence of cardiovascular diseases, strokes, and type 2 diabetes [64, 65]. This diet has been observed to promote the expansion of protective microbes releasing intestinal metabolites such as SCFA [66] and showed a decrease in Firmicutes and Bacteroidetes and a significant reduction in lipopolysaccharide concentration [67].

The ketogenic diet is known as a very low-carbohydrate and high-fat diet, inducing ketosis [68]. Due to the negative reputation of fats regarding the risk of developing cardiovascular diseases, there is much controversy surrounding the impact of this diet on such conditions; however, ketogenic diets have been shown to improve cardiovascular risk factors, including blood glucose, body weight, triglycerides, and HDL levels, in studies lasting 6 months. However, most of these improvements were no longer significant after 12 months [69]. The impact of this diet on the microbiota is unclear, with some human and animal studies yielding different results, demonstrating positive effects on the remodeling of bacterial architecture and intestinal biological functions. In contrast, others report negative effects such as lower diversity and an increased amount of proinflammatory bacteria [70]. Additionally, with this diet, the intestinal microbiome structure in epileptic infants differs drastically from that of healthy infants. Proteobacteria, significantly higher in epileptic infants, decreased sharply after Crohn’s disease. Cronobacter predominated in the epileptic infant group and remained at a low level in both healthy and epileptic infants after Crohn’s disease. Bacteroides significantly increased in epileptic infants after Crohn’s disease, whereas Prevotella and Bifidobacterium also grew in number and continued to increase [71]. The ketogenic diet strictly influences taxa, richness, and diversity of bacteria.

Fasting is the intentional cessation of solid food and stimulant intake over a limited period. Intermittent fasting has demonstrated beneficial effects on cardiovascular diseases, consistently showing a reduction in body weight, body fat mass, and BMI in individuals with obesity, type 2 diabetes, and high cardiovascular risk [72]. Time-restricted fasting enhances metabolic rhythms and prevents metabolic diseases such as obesity and inflammation, independently of caloric restriction [73]. The 5:2 diet (periodic fasting) has also proven effective in intermittent fasting programs for preventing and treating cardiometabolic diseases [72]. Some studies have shown that the 5:2 diet is more effective in glycemic control for patients with obesity, type 2 diabetes, and metabolic syndrome, achieving significant improvements in weight, blood pressure, and adiposity factors [74, 75]. Fasting may impact the composition and abundance of the human intestinal microbiota, with significant changes observed at the phylum, class, and species levels. One study identified changes in nine major phyla representing approximately 90% of operational taxonomic units in the intestinal microbiota, with Firmicutes and Bacteroidetes as predominant phyla. Additionally, a significant increase in the relative abundance of spirochetes was observed in the intermittent fasting group, while the majority of other phyla decreased. Furthermore, 23 species were identified as significantly affected after intermittent fasting intervention, with some species increasing in abundance (e.g., Ruminococcus gnavus and Roseburia faecis) and others decreasing [74].

Certain dietary patterns are less favorable for the microbiome and metabolic syndrome, as they are associated with obesity and subsequent diabetes and cardiovascular diseases. An example is the consumption of diets with artificial sweeteners, which may contribute to metabolic syndrome and obesity while negatively altering the host’s microbiome [76]. Excessive consumption of simple carbohydrates can also pose problems, leading to reduced microbial diversity, particularly a decrease in Bacteroidetes and an increase in Proteobacteria [77]. There is also an increase in the Firmicutes/Bacteroidetes ratio, causing intestinal permeability disturbance and an increase in inflammatory cytokines (causing colitis) [78]. The American or Western diet (high in fat, animal protein, and sugar) induces dysbiosis, negative effects on gastrointestinal mucosa, inflammation, and an increase in bile-resistant microorganisms such as Alistipes, Bilophila, and Bacteroides, while reducing the levels of Firmicutes necessary for vegetable metabolism (Roseburia, Eubacterium rectale, and Ruminococcus bromii) [62]. Finally, alcohol consumption is associated with changes in the intestinal microbiota. Although evidence in humans is limited, various studies suggest that alcohol-induced alterations in the composition and metabolic function of the gastrointestinal microbiota may contribute to the well-established link between alcohol-induced oxidative stress, increased intestinal permeability to bacterial products, and the subsequent development of liver diseases and other conditions. Alcohol is associated with quantitative and qualitative changes in the intestinal microbiota. These changes may be linked to increased inflammation in the gastrointestinal tract, increased intestinal permeability resulting in the presence of bacterial toxins in the blood, systemic inflammation, and damage to tissues or organs [79].

5. Conclusions

Substantial evidence was provided by this chapter supporting the hypothesis that the gut microbiota could be beneficially modulated by probiotics, thereby positively influencing the management of metabolic syndrome. Aligned with the study’s general objective, the significant scholarly focus on the interplay between diet, probiotics, and metabolic health was highlighted through bibliometric analysis and thematic exploration of research data from 2014 to 2023. The key findings emphasized the therapeutic potential of probiotics in inflammation reduction, immunity improvement, oxidative stress mitigation, and lipid concentration normalization, critical factors in the pathophysiology of metabolic syndrome. Additionally, dietary patterns, particularly Mediterranean and plant-based diets, were shown to positively impact cardiovascular health and diabetes management through their modulatory effects on the intestinal microbiota. These results validated the initial hypothesis and provided a comprehensive understanding of the current research landscape, offering valuable insights for future research directions and interventions to improve metabolic health through dietary and probiotic strategies.

The perspectives on this line of research are promising and suggest a dynamic and evolving field with a strong focus on the interplay between diet, probiotics, and metabolic health. The robust annual growth rate of 31.8% in scholarly output and the global dimension of international co-authorships at 23.91% indicate a burgeoning interest and collaborative efforts in this area. The significant attention given to the use of probiotics in managing metabolic syndrome and its associated conditions and the variability in study results points to the need for further research to solidify the evidence base and understand the mechanisms at play. The scientometric mapping and word cloud analysis reveal that cardiovascular and metabolic health, women’s health, and gut microbiota are key thematic areas, suggesting these will continue to be significant topics of investigation. Systematic reviews and meta-analyses indicate an ongoing effort to synthesize and evaluate existing research, which is critical for advancing the field and informing clinical practice. Overall, the perspectives highlight the importance of continued research into the nuanced effects of probiotics on metabolic health, with an emphasis on experimental design, participant characteristics, probiotic strains, and dosages to address the current lack of consensus and to harness the full potential of probiotics in metabolic syndrome management.

Acknowledgments

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of interest

The authors declare no conflict of interest.

Thanks

The authors thank the Sistema Nacional de Investigadoras e Investigadores (SNII-CONAHCyT), the Universidad Autónoma del Estado de Hidalgo (UAEH), the Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM) for the support provided to carry out this book chapter.