Skip to main content

Advertisement

Global research trends in microbiome-gut-brain axis during 2009–2018: a bibliometric and visualized study

BMC Gastroenterology volume 19, Article number: 158 (2019) | Download Citation

Article metrics

Abstract

Background

The pathways and mechanism by which associations between the gut microbiome and the brain, termed the microbiome-gut-brain axis (MGBA), are manifest but remain to be fully elucidated. This study aims to use bibliometric analysis to estimate the global activity within this rapidly developing field and to identify particular areas of focus that are of current relevance to the MGBA during the last decade (2009–2018).

Methods

The current study uses the Scopus for data collection. We used the key terms “microbiome-gut-brain axis” and its synonyms because we are concerned with MGBA per se as a new concept in research rather than related topics. A VOSviewer version 1.6.11 was used to visualize collaboration pattern between countries and authors, and evolving research topics by analysis of the term co-occurrence in the title and abstract of publications.

Results

Between 2009 and 2018, there were 51,504 published documents related to the microbiome, including 1713 articles related to the MGBA: 829 (48.4%) original articles, 658(38.4%) reviews, and 226 (13.2%) other articles such as notes, editorials or letters. The USA took the first place with 385 appearances, followed by Ireland (n = 161), China (n = 155), and Canada (n = 144).The overall citation h-index was 106, and the countries with the highest h-index values were the USA (69), Ireland (58), and Canada (43). The cluster analysis demonstrated that the dominant fields of the MGBA include four clusters with four research directions: “modeling MGBA in animal systems”, “interplay between the gut microbiota and the immune system”, “irritable bowel syndrome related to gut microbiota”, and “neurodegenerative diseases related to gut microbiota”.

Conclusions

This study demonstrates that the research on the MGBA has been becoming progressively more extensive at global level over the past 10 years. Overall, our study found that a large amount of work on MGBA focused on immunomodulation, irritable bowel syndrome, and neurodevelopmental disorders. Despite considerable progress illustrating the communication between the gut microbiome and the brain over the past 10 years, many issues remain about their relevance for therapeutic intervention of many diseases.

Open Peer Review reports

Background

The interaction between gut and brain has been acknowledged by physicians since antiquity [1]. As far back as the sixteenth century, the association between depression and altered bowel function was recognized and in 1978 Manning and his colleagues described the “irritable bowel syndrome (IBS)” as a gastrointestinal condition which is strongly associated with psychological stress, some authors reporting 50% of sufferers have comorbid depression or anxiety [2]. The pathways and mechanism by which these associations are manifest remain to be fully elucidated. However, recent developments in genome sequencing, metabolomics, functional imaging and computational biology have increased our understanding considerably [3,4,5,6].

The rapid development of 16S ribosomal RNA and whole genome sequencing analysis has enabled us to understand the diverse nature of the microbial symbionts that inhabit our gastrointestinal tract [7,8,9]. Metabolomics is beginning to explain how those microbes produce a range of molecules that impact our behaviors and perceptions. The changes in our microbial diversity, manifest as changes in their metabolic output appear to alter the development of multiple facets of the enteric and central nervous systems including astrocytes, microglial cells and neurons [10, 11]. Functional imaging, functional magnetic resonance imaging and magneto encephalography, have enabled us to identify real time changes in neurological activity and correlate these with changes in behavior or perception [12,13,14]. Advances in computational biology are beginning to explain how these multifaceted and complex systems interact with each other [15, 16].

The microbiota interacts with the host through their effect on immune, neuro-hormonal and neural pathways. They have been shown to impact a broad range of disease, including neurodegenerative disorders, such as multiple sclerosis and Parkinson’s disease, auto-immune disease and obesity [17, 18]. The gastrointestinal microbiome has also been shown to influence behavior in mammals and man [19, 20]. Transfer of feces from depressed humans to microbiota depleted rats led the recipient rats to display behaviors analogous to depression in the human (anhedonia and anxiety like behaviors) [21, 22]. A strain of bifidobacteria has been demonstrated to increase resilience in people with anxiety [23]. These findings were not observed when healthy people consumed a strain of Lactobacillus [24]. Short chain fatty acids, propionate, butyrate and acetate, are important products of the microbiome and changes in the proportion and quantities of these products alter insulin resistance, ghrelin production and presumably appetite and risk of obesity and diabetes [25, 26].

Bibliometric analyses have been used in various fields to highlight the most influential countries, authors, journals, publications, and institutions [27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42]. These include research related to microbiota [43, 44]. Worldwide, there are more than 330 clinical studies recorded on clinical trials.gov with a specific focus on the microbiome. This is a growing area of importance in order to better understand the impact of specific strains on individuals, and the interaction with pre-existing microbial symbionts. Currently, there is a lack of research concerning assessment of the current status, hot spots, and future outlook on the theme of the microbiome-gut-brain axis (MGBA). This study aims to use bibliometric methods to identify particular areas of research activity in this field and to allow researchers to identify new areas for future development.

Methods

Although a large number of databases are used for evaluation research at global level [45,46,47], the current study uses the Scopus database which is widely accepted among researchers for the purposes of high quality bibliometric analyses [44, 48,49,50,51,52,53]. Scopus is the world’s largest abstract and citation database of peer-reviewed research literature, and is an established resource for identifying biomedical research including MEDLINE documents, and includes a higher level of detail than PubMed including the country of origin and citations per document [47, 54].

We used the key terms “microbiome-gut-brain axis” and its synonyms because we are concerned with microbiome-gut-brain axis per se as a new concept in research rather than related topics. Data mining was conducted on July 12, 2019. The central theme in this study was research articles containing “microbiome or microbiota and brain-gut or gut-brain” to identify items based on their search in the fields title, abstract and keyword simultaneously and the time was 10 years between 2009 and 2018.

Data analysis

VOSviewer software (www.vosviewer.com, Van Eck & Waltman version 1.6.11) was used to create a visual representation of collaborations between countries and authors using network maps [55]. Creating a term co-occurrence map in VOSviewer involved only terms that occurred in the title and abstract at least 50 times under binary counting [55]. Terms with the highest relevance score were used to create a term map for network visualization. The algorithm was designed to ensure that terms that co-occurred more frequently had larger bubbles and terms that have a high similarity are located close to each other [55].

Statistical analysis was carried out for the retrieved data by the Statistical Package for the Social Sciences (version 16.0, SPSS Inc., Chicago, IL, USA). Pearson correlation Coefficient was used to test the correlation between some variables (e.g. h-index and number of publications for each country, number of publications and years, and the number of publications related to MGBA and the number of publications related to microbiome in all fields). The analyses carried out in the current study focused largely on the frequencies and percentages of publications for types of documents, countries, journals, and institutes.

Results

Between 2009 and 2018, there were 51,504 published documents related to the microbiome, including 1713 articles related to the MGBA: 829 (48.4%) original articles, 658(38.4%) reviews, and 226 (13.2%) other articles such as notes, editorials or letters. English was the most frequently used language (n = 1648), followed by French (n = 16), and Chinese (n = 19), with these accounting for 98.2% of publications related to MGBA. Publications related to MGBA and the microbiome are represented in Fig. 1a and b, respectively. Time trend analyses show rising numbers of publications related to MGBA between 2009 and 2018 (r = 0.950; P value< 0.001), and a correlation between overall numbers of microbiome and MGBA publications (r = 0.991, p < 0.001) during the study period.

Fig. 1
figure1

Quantitative growth process of the publications concerning microbiome-gut-brain axis (a) and microbiome in all fields (b) in the period of 10 years

Full size image

The term analyses maps are presented in Fig. 2: the larger circles representing frequently occurring abstract and title terms. Colors used to differentiate between 4 main topic clusters: 1. “modeling MGBA in animal systems (red cluster)”, 2. “interplay between the gut microbiota and the immune system (green cluster)”, 3. “irritable bowel syndrome related to gut microbiota (blue cluster)”, and 4. “neurodegenerative diseases related to gut microbiota (yellow cluster)”.

Fig. 2
figure2

Research topics clustered by mapping of co-occurrences of terms in title/abstract for publications related to microbiome-gut-brain axis (MGBA). Of the 30,250 terms, 179 terms occurred at least 50 times. For each of the 179 terms, a relevance score was calculated and used to select the 60% most relevant terms. In Fig. 2, the size of the circles represents the occurrences of terms in title/abstract. The largest set of connected terms consists of 107 terms in four clusters. The four clusters can be broadly interpreted as “modeling MGBA in animal systems (red cluster)”, “interplay between the gut microbiota and the immune system (green cluster)”, “irritable bowel syndrome related to gut microbiota (blue cluster)”, and “neurodegenerative diseases related to gut microbiota (yellow cluster)”

Full size image

Table 1 presents the 10 most prolific countries related to MGBA publications, with the top 4 being the USA (n = 385), Ireland (n = 161), China (n = 155), and Canada (n = 144). The overall citation h-index was 106, and the countries with the highest h-index values were the USA (69), Ireland (58), and Canada (43). There is a positive modest correlation between h-index and number of published articles (r = 0.817, P-value = 0.004). Figure 3 shows the network visualization map for country collaborations, showing 35 out of a total 86 countries that had more than ten publications; the size of frame represents the number of publications, the thickness of lines signifies the extent of collaboration between the countries.

Table 1 Ten leading countries in the publications concerning microbiome-gut-brain axis
Full size table
Fig. 3
figure3

Network visualization map for country collaboration. Of the 86 countries, 35 had at least ten publications; the largest set of connected countries consists of 34 countries. The size of frame represents the number of publications of the country and the thickness of lines signifies the size of collaboration between the countries, while 6 different colors seen in this figure represent the collaboration cluster of the countries

Full size image

Co-authorship in the field of MGBA is shown in Fig. 4, with 5 clusters identified; the size of frame represents the number of publications by an author, and the thickness of lines signifies the extent of collaboration between authors. Of the 6054 authors, 25 had at least ten publications including the most active author Cryan, J.F. with 120 (7.0%) publications.

Fig. 4
figure4

Network visualization map for author collaboration. Of the 6054 authors, 25 had at least ten publications; the largest set of connected authors consists of 20 authors. The size of frame represents the number of publications of the author and the thickness of lines signifies the size of collaboration between the authors, while 5 different colors seen in this figure represent the collaboration cluster of the authors

Full size image

The 10 most influential journals covering the MGBA research with their IFs are shown in Table 2. The three most influential journals from the top 10 influential journals are Brain Behavior and Immunity (49 articles), Plos One (34 articles), and Scientific Reports (33 articles). Table 3 shows the list of top 20 most-cited articles [56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75] on MGBA. The most prolific institutions were University College Cork (152 articles), McMaster University (67 articles), and INSERM (Institut National de la Santé et de la Recherche Médicale, French National Institute of Health and Medical Research, 43 articles) (Table 4).

Table 2 The most productive journals in the microbiome-gut-brain axis research
Full size table
Table 3 The 20 most influential articles in the microbiome-gut-brain axis research
Full size table
Table 4 The top ten most productive institutes
Full size table

Discussion

This is the first application of bibliometric quantitatively and qualitatively methods regarding the MGBA involving 1713 papers retrieved from Scopus. The results of this bibliometric analysis present a comprehensive overview of the development of the scientific literature in the MGBA field over the past 10 years.

The number of articles concerning MGBA research increased rapidly between 2009 and 2018. This increase is likely related to the many experts in psychiatry, neurology and gastroenterology fields (e.g. Cryan J.F., Dinan T.G., Clarke G., Bienenstock J., Forsythe P., Stanton C., Quigley E.M.M., Bercik P., O’Mahony S.M., Shanahan F., Foster J.A., Moloney R.D., and others) developing their interest in the physiological role of the guts’ microbiota on brain and behavior as an emerging platform for therapeutic intervention of many diseases. Furthermore, the increased number of publications may relate to several hot topics [56,57,58,59,60,61,62,63,64,65,66,67,68, 70,71,72, 74,75,76,77] which were published during this period, revealing novel findings that open the door for new areas of investigation. These studies propose novel concepts for treating several conditions such as IBS, autism, depression, multiple sclerosis, auto-immune disease, Parkinson’s disease, and obesity [78,79,80,81,82,83,84,85].

Since 2012, there has been growing research output in the field of MGBA, which is consistent with increasing research activity related to the microbiome in general. Similar findings have been reported in other bibliometric studies [43, 44, 86,87,88,89]. A possible underlying explanation for the rising publication numbers is that in 2013 the National Institutes of Health (NIH) launched the second phase of Integrative Human Microbiome Project (iHMP) [90].

Research output related to MGBA most often originated from the United States, as reported in other bibliometric studies regarding microbiome research [43, 44, 86,87,88,89]. Our study clearly reveals that the United States is at the forefront of studies on MGBA. The research output from the USA may be associated with the wide range of researchers with an interest within this field and a substantial amount of financial support to researchers. In 2013 the USA launched a special research project on gut microbiota-brain axis [91]. Since then, there has been increasing neuroscience interest in the role of gut microbiota on animal and human brain behavior and cognitive development [92, 93]. Ireland featured as the second most prolific nation and this might be related to Professor John F Cryan and Professor Ted Dinan, with their team who are the most active authors in this field, and principal investigators at the Alimentary Pharmabiotic Centre (APC) in University College Cork. [94] The APC is funded by Science Foundation Ireland (SFI) [75], and has conducted studies in collaboration with several companies including GlaxoSmithKline, Cremo, Suntory, Pfizer, Wyeth and Mead Johnson which consequently provided more funding for conducting research in the field of psychobiotics [75], thus may contribute to increasing number of publications regarding gut microbiota-brain axis.

The number of citations for the top 20 articles in the current study varied from 1490 to 347, which is higher range of citations than in other medical fields such as mobile-health [95], toxicology [28], social media in psychology [96], parasitic diseases [51, 97], and viral diseases [98,99,100]. Additionally, it also reveals that researchers paid great attention on the MGBA mostly in recent years, and published several outstanding articles on top-ranking journals in the medical field such as Science [56] and Nature [64]. The most cited article is “Host-gut microbiota metabolic interactions” a review by Nicholson et al., 2012 [56], published in Science, where the authors suggest that the manipulation of the gut microbiota to optimize new therapeutic strategies could control many diseases and improve health. The second most cited article “Mind-altering microorganisms: The impact of the gut microbiota on brain and behavior” was published in the Nature Reviews Neuroscience in 2012 by Cryan and Dinan [57], where the authors suggest that the concept of a microbiota-gut-brain axis may lead to the development of novel therapeutics for management of several neurological and psychiatric disorders.

Finally, there are some limitations for our study findings. First, the search was limited to publications listed in Scopus, which is the largest biomedical database and the most frequently used database for bibliometric analyses, although it might not contain all publications relevant to MGBA research. MGBA publications that do not include this term or its synonyms in the title, abstract or key words might not be taken into account for our analysis. Secondly, a general limitation of the bibliometric approach is that there is no weighting to take account of the quality or scientific rigor of any individual publication. Despite these limitations, we still consider that the findings offer a valid representation of MGBA research output at a global level.

Conclusions

The characteristics of the MGBA related publications from 2009 to 2018 are investigated through the bibliometrics analysis based on the Scopus database. This study demonstrates that the research on the MGBA has been becoming progressively more extensive at global level over the past 10 years. Overall, our study found a large amount of work on MGBA, focused on immunomodulation, irritable bowel syndrome, and neurodevelopmental disorders. Despite considerable progress illustrating the communication between the gut microbiome and the brain over the past 10 years, many issues remain to fully realize their relevance for therapeutic intervention of many diseases.

Availability of data and materials

Not applicable.

Abbreviations

IBS:

Irritable bowel syndrome

IFs:

Impact factors

JCR:

Journal citation reports

SCR:

Standard competition ranking

SPSS:

Statistical package for social sciences

References

  1. 1.

    Miller I. The gut-brain axis: historical reflections. Microb Ecol Health Dis. 2018;29(1):1542921.

  2. 2.

    Manning AP, Thompson WG, Heaton KW, Morris AF. Towards positive diagnosis of the irritable bowel. Br Med J. 1978;2(6138):653–4.

  3. 3.

    Enaud R, Vandenborght LE, Coron N, Bazin T, Prevel R, Schaeverbeke T, Berger P, Fayon M, Lamireau T, Delhaes L. The mycobiome: a neglected component in the microbiota-gut-brain axis. Microorganisms. 2018;6(1):22.

  4. 4.

    Cussotto S, Clarke G, Dinan TG, Cryan JF. Psychotropics and the microbiome: a chamber of secrets. Psychopharmacology. 2019;236(5):1411–32.

  5. 5.

    Cussotto S, Sandhu KV, Dinan TG, Cryan JF. The neuroendocrinology of the microbiota-gut-brain axis: a behavioural perspective. Front Neuroendocrinol. 2018;51:80–101.

  6. 6.

    Cussotto S, Strain CR, Fouhy F, Strain RG, Peterson VL, Clarke G, Stanton C, Dinan TG, Cryan JF. Differential effects of psychotropic drugs on microbiome composition and gastrointestinal function. Psychopharmacology. 2019;236(5):1671–85.

  7. 7.

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14.

  8. 8.

    Human Microbiome Jumpstart Reference Strains Consortium. A catalog of reference genomes from the human microbiome. Science. 2010;328(5981):994–9.

  9. 9.

    Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486(7402):215–21.

  10. 10.

    Tang J. Microbial metabolomics. Curr Genomics. 2011;12(6):391–403.

  11. 11.

    Sekirov I, Russell SL, Antunes LC, Finlay BB. Gut microbiota in health and disease. Physiol Rev. 2010;90(3):859–904.

  12. 12.

    Wang H, Lee IS, Braun C, Enck P. Effect of probiotics on central nervous system functions in animals and humans: a systematic review. J Neurogastroenterol Motil. 2016;22(4):589–605.

  13. 13.

    Lener MS, Niciu MJ, Ballard ED, Park M, Park LT, Nugent AC, Zarate CA Jr. Glutamate and gamma-aminobutyric acid systems in the pathophysiology of major depression and antidepressant response to ketamine. Biol Psychiatry. 2017;81(10):886–97.

  14. 14.

    Al Omran Y, Aziz Q. Functional brain imaging in gastroenterology: to new beginnings. Nat Rev Gastroenterol Hepatol. 2014;11(9):565–76.

  15. 15.

    Malan-Muller S, Valles-Colomer M, Raes J, Lowry CA, Seedat S, Hemmings SMJ. The gut microbiome and mental health: implications for anxiety- and trauma-related disorders. OMICS. 2018;22(2):90–107.

  16. 16.

    Cong X, Henderson WA, Graf J, McGrath JM. Early life experience and gut microbiome: the brain-gut-microbiota signaling system. Adv Neonatal Care. 2015;15(5):314–23 quiz E311–312.

  17. 17.

    Tlaskalova-Hogenova H, Stepankova R, Kozakova H, Hudcovic T, Vannucci L, Tuckova L, Rossmann P, Hrncir T, Kverka M, Zakostelska Z, et al. The role of gut microbiota (commensal bacteria) and the mucosal barrier in the pathogenesis of inflammatory and autoimmune diseases and cancer: contribution of germ-free and gnotobiotic animal models of human diseases. Cell Mol Immunol. 2011;8(2):110–20.

  18. 18.

    Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell. 2014;157(1):121–41.

  19. 19.

    Zheng P, Zeng B, Zhou C, Liu M, Fang Z, Xu X, Zeng L, Chen J, Fan S, Du X, et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host's metabolism. Mol Psychiatry. 2016;21(6):786–96.

  20. 20.

    Dill-McFarland KA, Tang ZZ, Kemis JH, Kerby RL, Chen G, Palloni A, Sorenson T, Rey FE, Herd P. Close social relationships correlate with human gut microbiota composition. Sci Rep. 2019;9(1):703.

  21. 21.

    Kelly JR, Borre Y, O’Brien C, Patterson E, El Aidy S, Deane J, Kennedy PJ, Beers S, Scott K, Moloney G, et al. Transferring the blues: depression-associated gut microbiota induces neurobehavioural changes in the rat. J Psychiatr Res. 2016;82:109–18.

  22. 22.

    Wrzosek L, Ciocan D, Borentain P, Spatz M, Puchois V, Hugot C, Ferrere G, Mayeur C, Perlemuter G, Cassard AM. Transplantation of human microbiota into conventional mice durably reshapes the gut microbiota. Sci Rep. 2018;8(1):6854.

  23. 23.

    Okubo R, Koga M, Katsumata N, Odamaki T, Matsuyama S, Oka M, Narita H, Hashimoto N, Kusumi I, Xiao J, et al. Effect of bifidobacterium breve A−1 on anxiety and depressive symptoms in schizophrenia: a proof-of-concept study. J Affect Disord. 2019;245:377–85.

  24. 24.

    Kelly JR, Allen AP, Temko A, Hutch W, Kennedy PJ, Farid N, Murphy E, Boylan G, Bienenstock J, Cryan JF, et al. Lost in translation? The potential psychobiotic Lactobacillus rhamnosus (JB-1) fails to modulate stress or cognitive performance in healthy male subjects. Brain Behav Immun. 2017;61:50–9.

  25. 25.

    den Besten G, van Eunen K, Groen AK, Venema K, Reijngoud DJ, Bakker BM. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res. 2013;54(9):2325–40.

  26. 26.

    Kasubuchi M, Hasegawa S, Hiramatsu T, Ichimura A, Kimura I. Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation. Nutrients. 2015;7(4):2839–49.

  27. 27.

    Zyoud SH, Al-Jabi SW, Sweileh WM, Awang R, Waring WS. Global research productivity of N-acetylcysteine use in paracetamol overdose: a bibliometric analysis (1976-2012). Hum Exp Toxicol. 2015;34(10):1006–16.

  28. 28.

    Zyoud SH, Al-Jabi SW, Sweileh WM, Awang R, Waring WS. Bibliometric profile of the global scientific research on methanol poisoning (1902-2012). J Occup Med Toxicol. 2015;10:17.

  29. 29.

    Zyoud SH, Al-Jabi SW, Sweileh WM, Waring WS. Scientific research related to calcium channel blockers poisoning: bibliometric analysis in Scopus, 1968-2012. Hum Exp Toxicol. 2015;34(11):1162–70.

  30. 30.

    Zyoud SH, Waring WS, Al-Jabi SW, Sweileh WM. Global cocaine intoxication research trends during 1975-2015: a bibliometric analysis of web of science publications. Subst Abuse Treat Prev Policy. 2017;12(1):6.

  31. 31.

    Zyoud SH, Waring WS, Al-Jabi SW, Sweileh WM. Global research production in glyphosate intoxication from 1978 to 2015: a bibliometric analysis. Hum Exp Toxicol. 2017;36(10):997–1006.

  32. 32.

    Zyoud SH, Waring WS, Al-Jabi SW, Sweileh WM, Awang R. The 100 most influential publications in paracetamol poisoning treatment: a bibliometric analysis of human studies. Springerplus. 2016;5(1):1534.

  33. 33.

    Zyoud SH, Waring WS, Al-Jabi SW, Sweileh WM, Rahhal B, Awang R. Intravenous lipid emulsion as an antidote for the treatment of acute poisoning: a bibliometric analysis of human and animal studies. Basic Clin Pharmacol Toxicol. 2016;119(5):512–9.

  34. 34.

    Zyoud SH, Waring WS, Sweileh WM, Al-Jabi SW. Global research trends in lithium toxicity from 1913 to 2015: a bibliometric analysis. Basic Clin Pharmacol Toxicol. 2017;121(1):67–73.

  35. 35.

    Albuquerque PC, Castro MJ, Santos-Gandelman J, Oliveira AC, Peralta JM, Rodrigues ML. Bibliometric indicators of the zika outbreak. PLoS Negl Trop Dis. 2017;11(1):e0005132.

  36. 36.

    Sweileh WM, Al-Jabi SW, Sawalha AF, AbuTaha AS, Zyoud SH. Bibliometric analysis of publications on campylobacter: (2000-2015). PLoS Negl Trop Dis. 2016;35(1):39.

  37. 37.

    Sweileh WM, Al-Jabi SW, Zyoud SH, Sawalha AF, Abu-Taha AS. Global research output in antimicrobial resistance among uropathogens: a bibliometric analysis (2002-2016). J Glob Antimicrob Resist. 2018;13:104–14.

  38. 38.

    Sweileh WM, Shraim NY, Al-Jabi SW, Sawalha AF, AbuTaha AS, Zyoud SH. Bibliometric analysis of global scientific research on carbapenem resistance (1986-2015). Ann Clin Microbiol Antimicrob. 2016;15(1):56.

  39. 39.

    Sweileh WM, Shraim NY, Al-Jabi SW, Sawalha AF, Rahhal B, Khayyat RA, Zyoud SH. Assessing worldwide research activity on probiotics in pediatrics using Scopus database: 1994-2014. World Allergy Organ J. 2016;9:25.

  40. 40.

    Zhang M, Gao M, Yue S, Zheng T, Gao Z, Ma X, Wang Q. Global trends and future prospects of food waste research: a bibliometric analysis. Environ Sci Pollut Res Int. 2018;25(25):24600–10.

  41. 41.

    Zheng M, Fu HZ, Ho YS. Research trends and hotspots related to ammonia oxidation based on bibliometric analysis. Environ Sci Pollut Res Int. 2017;24(25):20409–21.

  42. 42.

    Zongyi Y, Dongying C, Baifeng L. Global regulatory T-cell research from 2000 to 2015: a bibliometric analysis. PLoS One. 2016;11(9):e0162099.

  43. 43.

    Tian J, Li M, Lian F, Tong X. The hundred most-cited publications in microbiota of diabetes research: a bibliometric analysis. Medicine (Baltimore). 2017;96(37):e7338.

  44. 44.

    Yao H, Wan JY, Wang CZ, Li L, Wang J, Li Y, Huang WH, Zeng J, Wang Q, Yuan CS. Bibliometric analysis of research on the role of intestinal microbiota in obesity. PeerJ. 2018;6:e5091.

  45. 45.

    Bakkalbasi N, Bauer K, Glover J, Wang L. Three options for citation tracking: google scholar, scopus and web of science. Biomed Digit Libr. 2006;3:7.

  46. 46.

    Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, scopus, web of science, and google scholar: strengths and weaknesses. FASEB J. 2008;22(2):338–42.

  47. 47.

    Kulkarni AV, Aziz B, Shams I, Busse JW. Comparisons of citations in web of science, scopus, and google scholar for articles published in general medical journals. Jama. 2009;302(10):1092–6.

  48. 48.

    Hernandez-Vasquez A, Alarcon-Ruiz CA, Bendezu-Quispe G, Comande D, Rosselli D. A bibliometric analysis of the global research on biosimilars. J Pharm Policy Pract. 2018;11:6.

  49. 49.

    Khalili M, Rahimi-Movaghar A, Shadloo B, Mojtabai R, Mann K, Amin-Esmaeili M. Global scientific production on illicit drug addiction: a two-decade analysis. Eur Addict Res. 2018;24(2):60–70.

  50. 50.

    Lee RP, Xu R, Dave P, Ajmera S, Lillard JC, Wallace D, Broussard A, Motiwala M, Norrdahl S, Howie C, et al. Taking the next step in publication productivity analysis in pediatric neurosurgery. J Neurosurg Pediatr. 2018;21(6):655–65.

  51. 51.

    Sweileh WM. Global output of research on epidermal parasitic skin diseases from 1967 to 2017. Infect Dis Poverty. 2018;7(1):74.

  52. 52.

    Teles RHG, Moralles HF, Cominetti MR. Global trends in nanomedicine research on triple negative breast cancer: a bibliometric analysis. Int J Nanomedicine. 2018;13:2321–36.

  53. 53.

    Zyoud SH. Investigating global trends in paraquat intoxication research from 1962 to 2015 using bibliometric analysis. Am J Ind Med. 2018;61(6):462–70.

  54. 54.

    Agarwal A, Durairajanayagam D, Tatagari S, Esteves SC, Harlev A, Henkel R, Roychoudhury S, Homa S, Puchalt NG, Ramasamy R, et al. Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian J Androl. 2016;18(2):296–309.

  55. 55.

    van Eck N, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2009;84(2):523–38.

  56. 56.

    Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, Pettersson S. Host-gut microbiota metabolic interactions. Science. 2012;336(6086):1262–7.

  57. 57.

    Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci. 2012;13(10):701–12.

  58. 58.

    Diaz Heijtz R, Wang S, Anuar F, Qian Y, Bjorkholm B, Samuelsson A, Hibberd ML, Forssberg H, Pettersson S. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci U S A. 2011;108(7):3047–52.

  59. 59.

    Hsiao EY, McBride SW, Hsien S, Sharon G, Hyde ER, McCue T, Codelli JA, Chow J, Reisman SE, Petrosino JF, et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell. 2013;155(7):1451–63.

  60. 60.

    Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG, Bienenstock J, Cryan JF. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad Sci U S A. 2011;108(38):16050–5.

  61. 61.

    Foster JA, McVey Neufeld KA. Gut-brain axis: how the microbiome influences anxiety and depression. Trends Neurosci. 2013;36(5):305–12.

  62. 62.

    Bercik P, Denou E, Collins J, Jackson W, Lu J, Jury J, Deng Y, Blennerhassett P, Macri J, McCoy KD, et al. The intestinal microbiota affect central levels of brain-derived neurotropic factor and behavior in mice. Gastroenterology. 2011;141(2):599–609 609.e591–593.

  63. 63.

    Collins SM, Surette M, Bercik P. The interplay between the intestinal microbiota and the brain. Nat Rev Microbiol. 2012;10(11):735–42.

  64. 64.

    Berer K, Mues M, Koutrolos M, Rasbi ZA, Boziki M, Johner C, Wekerle H, Krishnamoorthy G. Commensal microbiota and myelin autoantigen cooperate to trigger autoimmune demyelination. Nature. 2011;479(7374):538–41.

  65. 65.

    De Vadder F, Kovatcheva-Datchary P, Goncalves D, Vinera J, Zitoun C, Duchampt A, Backhed F, Mithieux G. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell. 2014;156(1–2):84–96.

  66. 66.

    Neufeld KM, Kang N, Bienenstock J, Foster JA. Reduced anxiety-like behavior and central neurochemical change in germ-free mice. Neurogastroenterol Motil. 2011;23(3):255–64 e119.

  67. 67.

    O'Mahony SM, Marchesi JR, Scully P, Codling C, Ceolho AM, Quigley EM, Cryan JF, Dinan TG. Early life stress alters behavior, immunity, and microbiota in rats: implications for irritable bowel syndrome and psychiatric illnesses. Biol Psychiatry. 2009;65(3):263–7.

  68. 68.

    Clarke G, Grenham S, Scully P, Fitzgerald P, Moloney RD, Shanahan F, Dinan TG, Cryan JF. The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner. Mol Psychiatry. 2013;18(6):666–73.

  69. 69.

    Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, Challis C, Schretter CE, Rocha S, Gradinaru V, et al. Gut microbiota regulate motor deficits and neuroinflammation in a model of parkinson’s disease. Cell. 2016;167(6):1469–1480.e1412.

  70. 70.

    Tillisch K, Labus J, Kilpatrick L, Jiang Z, Stains J, Ebrat B, Guyonnet D, Legrain-Raspaud S, Trotin B, Naliboff B, et al. Consumption of fermented milk product with probiotic modulates brain activity. Gastroenterology. 2013;144(7):1394–401 1401.e1391–1394.

  71. 71.

    Rhee SH, Pothoulakis C, Mayer EA. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol. 2009;6(5):306–14.

  72. 72.

    Braniste V, Al-Asmakh M, Kowal C, Anuar F, Abbaspour A, Toth M, Korecka A, Bakocevic N, Ng LG, Kundu P, et al. The gut microbiota influences blood-brain barrier permeability in mice. Sci Transl Med. 2014;6(263):263ra158.

  73. 73.

    Scheperjans F, Aho V, Pereira PA, Koskinen K, Paulin L, Pekkonen E, Haapaniemi E, Kaakkola S, Eerola-Rautio J, Pohja M, et al. Gut microbiota are related to Parkinson's disease and clinical phenotype. Mov Disord. 2015;30(3):350–8.

  74. 74.

    O'Mahony SM, Clarke G, Borre YE, Dinan TG, Cryan JF. Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behav Brain Res. 2015;277:32–48.

  75. 75.

    Cryan JF, O'Mahony SM. The microbiome-gut-brain axis: from bowel to behavior. Neurogastroenterol Motil. 2011;23(3):187–92.

  76. 76.

    Desbonnet L, Garrett L, Clarke G, Kiely B, Cryan JF, Dinan TG. Effects of the probiotic Bifidobacterium infantis in the maternal separation model of depression. Neuroscience. 2010;170(4):1179–88.

  77. 77.

    Grenham S, Clarke G, Cryan JF, Dinan TG. Brain-gut-microbe communication in health and disease. Front Physiol. 2011;2:94.

  78. 78.

    Bonaz BL, Bernstein CN. Brain-gut interactions in inflammatory bowel disease. Gastroenterology. 2013;144(1):36–49.

  79. 79.

    Dinan TG, Cryan JF. The microbiome-gut-brain axis in health and disease. Gastroenterol Clin N Am. 2017;46(1):77–89.

  80. 80.

    Dinan TG, Stanton C, Cryan JF. Psychobiotics: a novel class of psychotropic. Biol Psychiatry. 2013;74(10):720–6.

  81. 81.

    Evrensel A, Ceylan ME. The gut-brain axis: the missing link in depression. Clin Psychopharmacol Neurosci. 2015;13(3):239–44.

  82. 82.

    Mayer EA, Tillisch K, Gupta A. Gut/brain axis and the microbiota. J Clin Invest. 2015;125(3):926–38.

  83. 83.

    Misra S, Mohanty D. Psychobiotics: a new approach for treating mental illness? Crit Rev Food Sci Nutr. 2019;59(8):1230–36.

  84. 84.

    Petra AI, Panagiotidou S, Hatziagelaki E, Stewart JM, Conti P, Theoharides TC. Gut-microbiota-brain axis and its effect on neuropsychiatric disorders with suspected immune dysregulation. Clin Ther. 2015;37(5):984–95.

  85. 85.

    Sarkar A, Lehto SM, Harty S, Dinan TG, Cryan JF, Burnet PWJ. Psychobiotics and the manipulation of bacteria-gut-brain signals. Trends Neurosci. 2016;39(11):763–81.

  86. 86.

    Huang X, Fan X, Ying J, Chen S. Emerging trends and research foci in gastrointestinal microbiome. J Transl Med. 2019;17(1):67.

  87. 87.

    Ding-Qi B, Hui-Bo C, Xin-Yang L, Yi Z, Lan-Hua L, Yun-Hai G. Analysis of research status and hotspots of snail intestinal flora based on bibliometrics. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi. 2018;30(5):571–4.

  88. 88.

    Ejtahed HS, Tabatabaei-Malazy O, Soroush AR, Hasani-Ranjbar S, Siadat SD, Raes J, Larijani B. Worldwide trends in scientific publications on association of gut microbiota with obesity. Iran J Basic Med Sci. 2019;22(1):65–71.

  89. 89.

    Li Y, Zou Z, Bian X, Huang Y, Wang Y, Yang C, Zhao J, Xie L. Fecal microbiota transplantation research output from 2004 to 2017: a bibliometric analysis. PeerJ. 2019;7:e6411.

  90. 90.

    Integrative HMP (iHMP) Research Network Consortium. The Integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe. 2014;16(3):276–89.

  91. 91.

    Wang HX, Wang YP. Gut Microbiota-brain Axis. Chin Med J. 2016;129(19):2373–80.

  92. 92.

    Schmidt C. Mental health: thinking from the gut. Nature. 2015;518(7540):S12–5.

  93. 93.

    Smith PA. The tantalizing links between gut microbes and the brain. Nature. 2015;526(7573):312–4.

  94. 94.

    O'Connor A. The Psychobiotic revolution. Lancet Gastroenterol Hepatol. 2017;2(12):854.

  95. 95.

    Sweileh WM, Al-Jabi SW, AbuTaha AS, Zyoud SH, Anayah FMA, Sawalha AF. Bibliometric analysis of worldwide scientific literature in mobile - health: 2006-2016. BMC Med Inform Decis Mak. 2017;17(1):72.

  96. 96.

    Zyoud SH, Sweileh WM, Awang R, Al-Jabi SW. Global trends in research related to social media in psychology: mapping and bibliometric analysis. Int J Ment Heal Syst. 2018;12:4.

  97. 97.

    Zyoud SH. Global toxocariasis research trends from 1932 to 2015: a bibliometric analysis. Health Res Policy Syst. 2017;15(1):14.

  98. 98.

    Al-Jabi SW. Global research trends in West Nile virus from 1943 to 2016: a bibliometric analysis. Glob Health. 2017;13(1):55.

  99. 99.

    Sweileh WM. Global research trends of World Health Organization's top eight emerging pathogens. Glob Health. 2017;13(1):9.

  100. 100.

    Zyoud SH. Global research trends of Middle East respiratory syndrome coronavirus: a bibliometric analysis. BMC Infect Dis. 2016;16:255.

Download references

Acknowledgements

The authors would like to thank An-Najah National University for all administrative support throughout the implementation of this project.

Funding

No funding was received for writing this study.

Author information

Affiliations

  1. Poison Control and Drug Information Center (PCDIC), College of Medicine and Health Sciences, An-Najah National University, Nablus, 44839, Palestine

    • Sa’ed H. Zyoud

  2. Department of Clinical and Community Pharmacy, College of Medicine and Health Sciences, An-Najah National University, Nablus, 44839, Palestine

    • Sa’ed H. Zyoud
    •  & Samah W. Al-Jabi

  3. Clinical Research Centre, An-Najah National University Hospital, Nablus, 44839, Palestine

    • Sa’ed H. Zyoud

  4. Department of Gastroenterology, York Hospital, York Teaching Hospital NHS Foundation Trust, Wigginton Road, York, YO31 8HE, UK

    • Simon Smale

  5. Acute Medical Unit, York Teaching Hospitals NHS Foundation Trust, Wigginton Road, York, YO31 8HE, UK

    • W. Stephen Waring

  6. Department of Pharmacology and Toxicology, College of Medicine and Health Sciences, An-Najah National University, Nablus, 44839, Palestine

    • Waleed M. Sweileh

Authors

  1. Search for Sa’ed H. Zyoud in:

  2. Search for Simon Smale in:

  3. Search for W. Stephen Waring in:

  4. Search for Waleed M. Sweileh in:

  5. Search for Samah W. Al-Jabi in:

Contributions

SHZ designed the study, collected the data, analysed the data, and drafting the manuscript, SWA, WMS participated in the study design, involved in interpretation of the data, and made revisions to the initial draft, and SS and WSW contributed towards the conception, wrote part of the article, involved in interpretation of the data, and made revisions to the initial draft. All authors approved this final manuscript and also presented critical review before the final submission.

Corresponding author

Correspondence to Sa’ed H. Zyoud.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Keywords

  • Gut microbiota
  • Gut microbiome-brain axis
  • Microbiome
  • Bibliometric
  • Scopus

BMC Gastroenterology

ISSN: 1471-230X

Contact us