| Literature DB >> 35449051 |
Zhendong Wang1, Chen Bai1, Tingyao Hu2, Changyong Luo3, He Yu1, Xueyan Ma1, Tiegang Liu4, Xiaohong Gu5.
Abstract
BACKGROUND: Increasing attention has been paid to the potential relationship between gut and lung. The bacterial dysbiosis in respiratory tract and intestinal tract is related to inflammatory response and the progress of lung diseases, and the pulmonary diseases could be improved by regulating the intestinal microbiome. This study aims to generate the knowledge map to identify major the research hotspots and frontier areas in the field of gut-lung axis.Entities:
Keywords: Bibliometric; Gut–lung axis; Inflammation; Knowledge map
Mesh:
Year: 2022 PMID: 35449051 PMCID: PMC9022616 DOI: 10.1186/s12938-022-00987-8
Source DB: PubMed Journal: Biomed Eng Online ISSN: 1475-925X Impact factor: 3.903
Fig. 1Trend of publications in the field of gut–lung axis from 2011 to 2021
Top 10 funding sources
| Ranking | Funding source | Country/region | Frequency |
|---|---|---|---|
| 1 | United States Department of Health and Human Services | United States | 591 |
| 2 | National Institutes of Health | United States | 588 |
| 3 | National Natural Science Foundation of China | China | 300 |
| 4 | European Commission | Europe | 186 |
| 5 | National Heart, Lung, and Blood Institute | United States | 159 |
| 6 | National Institute of Allergy and Infectious Disease | United States | 152 |
| 7 | National Cancer Institution | United States | 129 |
| 8 | National Institute of Diabetes and Digestive and Kidney Diseases | United States | 103 |
| 9 | Ministry of Education, Culture, Sports, Science and Technology | Japan | 89 |
| 10 | Japan Society for the Promotion of Science | Japan | 84 |
Top 10 most publication journals
| Ranking | Journal | Frequency | IF |
|---|---|---|---|
| 1 | 91 | 3.24 | |
| 2 | 73 | 7.561 | |
| 3 | 45 | 4.379 | |
| 4 | 36 | 5.640 | |
| 5 | 25 | 5.923 | |
| 6 | 20 | 5.168* | |
| 7 | 19 | 7.313 | |
| 8 | 18 | 5.742 | |
| 9 | 17 | 5.422 | |
| 10 | 17 | 2.192 |
IF, impact factors in 2020. *, the impact factors of Oncotarget in 2016.
Top 10 most cited journals
| Ranking | Journals | Citation times | IF |
|---|---|---|---|
| 1 | 1586 | 3.24 | |
| 2 | 1409 | 11.205 | |
| 3 | 1364 | 49.962 | |
| 4 | 1184 | 47.728 | |
| 5 | 1044 | 91.245 | |
| 6 | 890 | 41.582 | |
| 7 | 878 | 21.405 | |
| 8 | 848 | 53.44 | |
| 9 | 833 | 5.422 | |
| 10 | 810 | 79.321 |
IF, impact factors in 2020
Fig. 2The dual-map overlay of gut–lung axis research. The dual-map overlay of journals represents the subject distribution of journals, with the left side of the graph representing citing journals and the right cited journals. The colored lines represent the citation relationship between articles in citing and in cited journals
Top 10 most publication countries
| Ranking | Country | Frequency |
|---|---|---|
| 1 | United States of America | 1035 |
| 2 | The People's Republic of China | 554 |
| 3 | The Federal Republic of Germany | 255 |
| 4 | The Republic of Italy | 203 |
| 5 | Japan | 190 |
| 6 | The United Kingdom of Great Britain and Northern Ireland | 176 |
| 7 | The Republic of France | 165 |
| 8 | Commonwealth of Australia | 153 |
| 9 | Canada | 147 |
| 10 | The Federative Republic of Brazil | 123 |
Fig. 3Co-occurrence analysis of countries. A Country co-occurrence map. B Cooperation network of the United States. C Cooperation network of China. The size of the node represents the number of publications. The link between nodes represents the existence of cooperation. The thickness of the lines represents the closeness of cooperation, and the thicker the lines are, the closer the cooperation is
Fig. 4Co-occurrence analysis of institutions. The size of the node represents the number of publications, the link between nodes represents the cooperation between institutions, and the thickness of the lines represents the degree of cooperation
Top 10 most publication institutions
| Ranking | Institution | Country/region | Frequency |
|---|---|---|---|
| 1 | The University of Michigan | United States | 48 |
| 2 | University of Washington | United States | 32 |
| 3 | Colorado State University | United States | 32 |
| 4 | Shanghai Jiao Tong University | China | 30 |
| 5 | Harvard University | United States | 30 |
| 6 | Johns Hopkins University | United States | 30 |
| 7 | Mayo Clinic | United States | 30 |
| 8 | Harvard Medical School | United States | 29 |
| 9 | Sun Yat-Sen University | China | 29 |
| 10 | University of Pittsburgh | United States | 29 |
Fig. 5Co-occurrence analysis of authors. The size of the node represents the number of publications, the link between nodes represents the cooperation between authors and the thickness of the link represents the degree of cooperation
Top 5 most publication authors
| Ranking | Frequency | Author | Country | Institution |
|---|---|---|---|---|
| 1 | 13 | Huffnagle Gary B | United States | University of Michigan Medical School |
| 2 | 12 | Dickson Robert P | United States | University of Michigan Medical School |
| 3 | 11 | Hansbro Philip M | Australia | The University of Newcastle |
| 4 | 11 | Marsland Benjamin J | Australia | Central Clinical School, Monash University |
| 5 | 8 | Shore Stephanie A | United States | Harvard University |
Top 40 keywords with the highest frequency of occurrence
| Ranking | Frequency | Year of first occurrence | Keyword |
|---|---|---|---|
| 1 | 362 | 2013 | Gut microbiota |
| 2 | 280 | 2011 | Inflammation |
| 3 | 239 | 2011 | Disease |
| 4 | 238 | 2011 | Infection |
| 5 | 226 | 2011 | Expression |
| 6 | 201 | 2013 | Microbiota |
| 7 | 195 | 2011 | Lung |
| 8 | 190 | 2014 | Microbiome |
| 9 | 179 | 2011 | Lung cancer |
| 10 | 167 | 2011 | Cancer |
| 11 | 154 | 2011 | Cell |
| 12 | 144 | 2011 | Asthma |
| 13 | 115 | 2015 | Intestinal microbiota |
| 14 | 106 | 2013 | Bacteria |
| 15 | 106 | 2011 | Mice |
| 16 | 105 | 2011 | In vitro |
| 17 | 100 | 2013 | Cystic fibrosis |
| 18 | 100 | 2012 | Activation |
| 19 | 99 | 2013 | T cell |
| 20 | 88 | 2011 | Mechanism |
| 21 | 86 | 2011 | Colorectal cancer |
| 22 | 85 | 2013 | Risk |
| 23 | 84 | 2011 | Identification |
| 24 | 83 | 2012 | Gut |
| 25 | 78 | 2011 | Immune response |
| 26 | 74 | 2015 | Immunity |
| 27 | 73 | 2011 | Pathogenesis |
| 28 | 73 | 2011 | Diagnosis |
| 29 | 72 | 2013 | Diversity |
| 30 | 70 | 2011 | Model |
| 31 | 67 | 2013 | Probiotics |
| 32 | 65 | 2011 | Oxidative stress |
| 33 | 62 | 2013 | Cell lung cancer |
| 34 | 62 | 2014 | Children |
| 35 | 58 | 2016 | Health |
| 36 | 58 | 2018 | Immunotherapy |
| 37 | 56 | 2015 | Biomarker |
| 38 | 55 | 2011 | Therapy |
| 39 | 54 | 2013 | Metabolism |
| 40 | 53 | 2011 | Obstructive Pulmonary disease |
Fig. 6Co-occurrence analysis of keywords. The node size represents the number of occurrences. The larger the nodes are, the more frequent the keyword occurs. The co-occurrence keywords are linked with the lines, their thickness represents the degree of connection
Fig. 7Top 25 keywords in burst impact. The blue and white squares in each row on the right side of the figure correspond to the year of hotspot. Red squares represent the year of hotspot, and blue squares represent non-hotspot year. The recent successive red squares represent the research hotspots in recent years
Fig. 8Co-occurrence analysis of references. The node size represents the citation frequency of the cited references, and the node with purple circle represents the key references. The larger purple circle indicates that the reference is more important
Fig. 9The cluster map of co-cited references. The cited references are clustered, each clustered box represents a category
Fig. 10Timeline map of clustering of co-cited references. The results of clusters are shown at the right side, the warm color indicates a more recent cluster, and the cold color indicates an earlier one
Top 10 most cited references
| Authors | Frequency | Year of publication | Journal | Title | Focus |
|---|---|---|---|---|---|
| Trompette A [ | 191 | 2014 | Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis | Fiber diet, bacterial metabolites, allergic airway disease | |
| Budden KF [ | 122 | 2017 | Emerging pathogenic links between microbiota and the gut–lung axis | Gut–lung axis | |
| Hilty M [ | 101 | 2010 | Disordered microbial communities in asthmatic airways | Dysbacteriosis of respiratory tract, asthmatic airways | |
| Tim J Schuijt [ | 94 | 2016 | The gut microbiota plays a protective role in the host defence against pneumococcal pneumonia | Bacterial pneumonia | |
| Erb-Downward JR [ | 88 | 2011 | Analysis of the lung microbiome in the “healthy” smoker and in COPD | Pulmonary microorganism, chronic obstructive pulmonary disease | |
| Emily S Charlson [ | 86 | 2011 | Topographical continuity of bacterial populations in the healthy human respiratory tract | Distribution of respiratory tract microbiome | |
| Takeshi Ichinohe [ | 86 | 2011 | Microbiota regulates immune defense against respiratory tract influenza A virus infection | Immunity after influenza virus infection | |
| Bertrand Routy [ | 84 | 2018 | Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors | Intestinal flora, immune checkpoint inhibitors | |
| Human Microbiome Project Consortium [ | 82 | 2012 | Structure, function and diversity of the healthy human microbiome | Structure, function and diversity of microbiome | |
| Benjamin J Marsland [ | 77 | 2015 | The Gut–Lung Axis in Respiratory Disease | Intestinal flora, Respiratory diseases |
Top 10 references ranked by centrality
| Author | Frequency | Year of publication | Journal | Title | Focus |
|---|---|---|---|---|---|
| Hilty M [ | 0.35 | 2010 | Disordered microbial communities in asthmatic airways | Dysbacteriosis of respiratory tract, asthmatic airways | |
| Mairi C Noverr [ | 0.34 | 2005 | Development of allergic airway disease in mice following antibiotic therapy and fungal microbiota increase: role of host genetics, antigen, and interleukin-13 | Antibiotic therapy, allergic airway, interleukin-13 | |
| Alison Morris [ | 0.32 | 2013 | Comparison of the respiratory microbiome in healthy nonsmokers and smokers | Differences of respiratory tract microbiome | |
| Paul Forsythe [ | 0.32 | 2007 | Oral Treatment with Live Lactobacillus reuteri Inhibits the Allergic Airway Response in Mice | Probiotics, allergic airway | |
| Emily S Charlson [ | 0.22 | 2011 | Topographical continuity of bacterial populations in the healthy human respiratory tract | Distribution of microorganisms in lung | |
| Torsten Olszak [ | 0.21 | 2012 | Microbial exposure during early life has persistent effects on natural killer T cell function | Microbial exposure during early life, natural killer T cells | |
| Christine M Bassis [ | 0.18 | 2015 | Analysis of the upper respiratory tract microbiotas as the source of the lung and gastric microbiotas in healthy individuals | Source of respiratory tract microorganism | |
| Trompette A [ | 0.17 | 2014 | Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis | Fiber diet, bacterial metabolites, allergic airway | |
| Jian Wang [ | 016 | 2014 | Respiratory influenza virus infection induces intestinal immune injury via microbiota-mediated Th17 cell-dependent inflammation | Influenza virus infection, Th17, intestinal immune injury | |
| Rebecca L Brown [ | 0.15 | 2017 | Rebecca L Brown | The microbiota protects against respiratory infection via GM-CSF signaling | Microbiota, GM-CSF signal, respiratory tract infection |
Fig. 11Top 25 cited references with burst impact. The blue and white squares in each row on the right side of the figure correspond to the year. The red squares represent that the references were highly cited in a short period and the recent successive red squares represent that the references were highly cited in recent years