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. 2026 Jan 28;11:100739. doi: 10.1016/j.puhip.2026.100739

A bibliometric mapping of research on antimicrobial resistance and mental health

Cyril Onwuelazu Uteh a,b,⁎, Ozgun Yetkin c,d, Claudia Dompe c,e, Ovinuchi Ejiohuo c,f,⁎⁎
PMCID: PMC12877849  PMID: 41659039

Abstract

Objective

Antimicrobial resistance and mental health are critical global health concerns, yet the intersection of these fields remains underexplored. This study presents a bibliometric analysis of research trends at the nexus of antimicrobial resistance and mental health.

Study design

Bibliometric study design.

Methods

Publications related to antimicrobial resistance and mental health from January 1, 2004 to March 13, 2025 were extracted from the Web of Science and Scopus databases. Bibliometric analysis and visualisation were performed using the R-Bibliometrix package (biblioshiny). We analysed publication patterns, citation metrics, and key research themes to identify leading topics, countries, institutions, authors, and journals contributing to this emerging area.

Results

The analysis included 3449 documents from 1397 sources, authored by 22,900 researchers, with an annual growth rate of 2.35 % and an average of 41.7 citations per paper. Output increased steadily after 2017, reaching its peak in 2020–2021. PLOS One was the most featured journal (78 articles), and the University of California, San Francisco, led institutional output (173 articles). The United States showed the highest collaboration and publication volume. Thematic and keyword analyses revealed dominant attention to depression, schizophrenia, bipolar disorder, and anxiety, alongside a smaller but growing focus on antimicrobial resistance, particularly its links to the gut–brain axis, where terms such as antibiotic resistance appeared less frequently but formed emerging clusters in recent years.

Conclusion

This study identifies research hotspots and gaps in the relationship between antimicrobial resistance and mental health, highlighting the need for public health policies that integrate mental health into antimicrobial resistance strategies. Prioritising this integration and enhancing global surveillance and collaboration are essential to addressing these interconnected challenges effectively.

Keywords: Antimicrobial resistance, Antibiotic resistance, Mental health, Mental disorders, Bibliometric analysis

1. Introduction

Antimicrobial Resistance (AMR) develops when bacteria, viruses, fungi, and parasites adapt (evolving over time to withstand antimicrobial medicines), reducing treatments' effectiveness, making infections more complicated to manage, and raising the risk of disease transmission, severe illness, and mortality [1]. The global rise in antimicrobial resistance threatens the effectiveness of treatments, with significant resistance seen in common gut microbiota such as Escherichia coli and Klebsiella pneumoniae. These bacteria, which are part of the intestinal flora, show alarming resistance to critical antibiotics, complicating the treatment of infections and increasing the risk of untreatable diseases [1].

Mental health disorders, which encompass a wide range of conditions (including depression, anxiety, bipolar disorder, and schizophrenia) affecting mood, thinking, and behaviour, are increasingly prevalent worldwide [2]. In 2019, an estimated 970 million people, or 1 in 8 globally, lived with a mental disorder, primarily anxiety and depression [2]. According to a World Health Organization (WHO) 2022 report, a 25 % rise in anxiety and depression was observed in the first year of the COVID-19 pandemic [3]. While this is expected, its significance lies in highlighting the widespread mental health impact of such global crises, the strain on healthcare systems, and the urgent need for improved mental health interventions and policies.

Ongoing research suggests that gut microbiota imbalances (dysbiosis) may impact brain function and mental health via the gut-brain axis (a communication network that connects the gut and the brain through various pathways, including the immune system, hormones, and neurotransmitters) [[4], [5], [6], [7], [8]]. The disruption of the gut microbiota can interfere with producing and regulating key neurotransmitters such as serotonin and dopamine, which are crucial for mood, sleep, and cognitive functions [[9], [10], [11]]. An unhealthy gut microbiota caused by AMR may increase the risk of mental health issues like depression and anxiety, contribute to a dysfunctional stress response, impair memory function, and potentially lead to neurodegenerative diseases such as Alzheimer's and Parkinson's [[12], [13], [14]]. Fig. 1 shows the impact of AMR on mental health through its influence on the gut microbiota.

Fig. 1.

Fig. 1

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Impact of antimicrobial resistance on mental health. Some text adapted from Ref. [15]. The bidirectional communication of the gut-brain axis contrasts a healthy gut microbiota (left) that maintains central nervous system (CNS) health via metabolic, immune, neural, and endocrine pathways, with an unhealthy gut microbiota (right) leading to disrupted neurotransmitter levels and increased risk of depression, anxiety, and neurodegenerative diseases. This unhealthy state is facilitated by Antimicrobial Resistance (AMR) disruption of the gut microbiota, involving reduced gut microbiome diversity, persistence of resistant bacteria, and resistant gene transfer. AMR – antimicrobial resistance, NF-kB - nuclear factor kappa-light-chain-enhancer of activated B cells, SCFA - Short-chain fatty acids, 5-HT - 5-hydroxytryptamine (serotonin), GABA - Gamma-Aminobutyric Acid.

The intersection between antimicrobial resistance and mental health presents a compelling yet complex scope of study within medical and psychiatric domains. Its implications are far-reaching, impacting not only the management of infectious diseases but also the broader aspects of healthcare, including mental health, such as strained resources and limited treatment options [1,16,17]. This interplay between antimicrobial resistance and mental health emerges through various pathways, including the biochemical, psychological, and socio-economic corridors. Biochemically, the effectiveness of psychotropic medications, especially those with antimicrobial properties such as certain atypical antipsychotics like quetiapine, can be compromised by antimicrobial resistance [18,19]. When exposed to these medications in the gut, the bacteria can develop resistance, rendering the drug ineffective. This potential reduction in medication efficacy may result in prolonged or exacerbated mental health issues such as stress, anxiety, and depression, posing challenges to treatment regimens and patient outcomes [20]. From a psychological perspective, the stress of dealing with persistent or recurrent infections due to antimicrobial-resistant organisms can exacerbate existing mental health conditions or potentially lead to new ones [16,21,22]. Socio-economically, AMR can strain healthcare systems, diverting resources from various sectors, including mental health services, and exacerbating the challenges faced by those seeking support for mental health issues. The increased burden of AMR can lead to longer hospital stays, higher medical costs, and greater emotional and financial strain on patients and their families [23,24], potentially worsening mental health outcomes. Environmentally, issues such as antibiotic overuse and misuse in humans, animals, and agriculture also contribute to antimicrobial resistance and intersect with broader determinants of mental health [25,26]. For instance, individuals living in areas with high levels of antibiotic resistance might experience longer hospital stays, inadequate healthcare access, and concerns about environmental health risks, and these experiences can result in increased anxiety and stress [27,28].

Therefore, research into the intersection of antimicrobial resistance and mental health is crucial for developing integrated approaches to address these challenges. Hence, this study aims to identify and analyse trends in this interdisciplinary research area, which can aid in understanding the complex relationships between AMR and mental health, guiding future studies, and informing public health policies and interventions designed to mitigate the impacts of AMR on mental health. Bibliometric analysis is crucial for identifying research trends, hotspots, and influential publications, enabling researchers to assess their output, impact, and emerging areas within a specific field. There is a lack of a systematic understanding of how antimicrobial resistance and mental health intersect, leaving their shared mechanisms, research patterns, and policy implications underexplored [29]. This bibliometric analysis presents a new, systematic mapping of the intersection between antimicrobial resistance and mental health, revealing overlooked connections, thematic gaps, and emerging priorities that can inform coordinated scientific, clinical, and policy responses. In summary, this study has the following objectives:

  • •

    To identify and analyse trends in the research on the intersection between antimicrobial resistance and mental health.

  • •

    To assess the growth, impact, and thematic focus of research in this area.

In doing this, we seek to answer the following research questions:

  • •

    What have the publication trends been over time in this field?

  • •

    Which countries, institutions, and authors are leading in this research?

  • •

    What are the most cited articles and journals?

  • •

    What are the key thematic areas and trends in the literature?

2. Methods

2.1. Study design

A bibliometric study design involves the quantitative analysis of patterns in the scientific literature using R programming software version 4.4.2. The R-Bibliometrix (Biblioshiny) version 5.1.1 was employed for visualisations, publication patterns, and descriptive statistics. Excel was used to filter titles and abstracts and retrieve DOI links to assess the relevance of studies. These tools provided valuable insights into publication networks and emerging research areas. Bibliometric analysis included criteria such as journals, publication year, authors, citations, institutions, and geographic distribution. Literature was sourced from Scopus and Web of Science (WoS). WoS is renowned for its accurate and comprehensive indexing, while Scopus offers broader metrics for assessing research impact [30,31]. Combining both datasets allowed for a more thorough analysis of our research objectives. The study methodology is illustrated in Fig. 1.

The search time frame spanned from January 1, 2004, to March 13, 2025, encompassing the last two decades. Using distinct sets of keywords relevant to the study context: ((“Antimicrobial resistance” OR “AMR” OR “Antibiotic resistance” OR “Drug resistance” OR “Multidrug resistance” OR “Resistant bacteria” OR “Superbugs” OR “Antimicrobial stewardship” OR “Antibiotic stewardship”) AND (“Mental health” OR “Mental disorders” OR “Psychiatric disorders” OR “Depression” OR “Anxiety” OR “Schizophrenia” OR “Bipolar disorder” OR “Psychosis” OR “Neuropsychiatric disorders” OR “Psychological stress” OR “Mental illness” OR “Cognitive disorders” OR “Psychiatric symptoms” OR “Psychological well-being”)). The data comprised peer-reviewed articles (original articles, reviews, and conference papers). Documents not written in English, letters, conference abstracts, editorial materials, news, book chapters, early access, and short surveys were excluded. The study selection involved screening titles, keywords, and abstracts for eligibility. The flow diagram of article selection is shown in Fig. 2. R programming was used to remove duplicates, and no further screening or independent review was needed, as the study analysed only metadata, not the content or quality of individual publications [32].

Fig. 2.

Fig. 2

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Flow diagram of article selection.

3. Results

As presented in Table 1, the analysis included 3449 documents from 1397 sources. It involved the contribution of 22,900 authors with an average citation of 41.7 per document. The annual growth rate of 2.35 % indicates a consistent increase in publications for the intersecting fields.

Table 1.

Summary of the main information of the study.

Description Results
MAIN INFORMATION ABOUT DATA
Timespan 2004:2025
Sources (Journals, Conference papers, etc) 1397
Documents 3449
Annual Growth Rate % 2.35
Document Average Age 9.27
Average citations per doc 41.74
References 0
DOCUMENT CONTENTS
Keywords Plus (ID) 22119
Author's Keywords (DE) 7549
AUTHORS
Authors 22900
Authors of single-authored docs 180
AUTHORS COLLABORATION
Single-authored docs 208
Co-Authors per Doc 8.97
International co-authorships % 14.53
DOCUMENT TYPES
Article 2512
Proceedings paper 34
Conference paper 33
Review 870
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3.1. Descriptive analysis

Fig. 3a shows the publication trend from 2004 to 2025. A reasonably consistent trend with minor fluctuating peaks is observed between 2005 and 2017. An upward trend was observed from 2017, with a sporadic upward trend from 2020 to 2021, followed by a declining trend.

Fig. 3.

Fig. 3

Fig. 3

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(a) Trends in the number of publications over time. N. of documents represents the Number of documents. (b) Top 10 most active authors. A circle represents an author, and the circle size represents the number of published documents. (c) Top 10 authors' production over time. Each circle represents one or more publications by an author in that specific year. The size of the circle represents the number of papers published by that author in that particular year. Larger circles indicate higher productivity in that year. The colour saturation (shading intensity) of the circle represents a citation or impact measure for those publications in that year. Darker circles suggest higher impact. (d) Top 10 authors' local impact. A circle represents an author, and the circle size represents the impact measure of each author. Impact Measure: H represents h-index or Hirsch index.

Among the authors (Fig. 3b), Wang Y stood out with 37 articles, followed by Zhang with 34 articles. In terms of their production over time (Fig. 3c), Wang Y and Zhang Y also stand out as top authors. Fig. 3d shows the author's local impact, which indicates the authors' influence on this intersecting field and is measured by the number of citations their work has received (indicated by H-index). The top author in terms of this is Bangsberg G, followed by Wang Y and Lin Y (Fig. 3d).

In Fig. 4a, Plos One is the top choice for publication in this field, contributing 78 articles, accounting for about 50 % of the total articles among the top 10 publishers. The Journal of Clinical Infectious Diseases followed this with 36 articles, the Journal of Electroconvulsive Therapy with 34 articles, and AIDS and the Journal of Clinical Psychiatry with 33 and 32 articles, respectively.

Fig. 4.

Fig. 4

Fig. 4

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(a) Leading journals publishing in the field. N. of documents represents the Number of documents (b), Top affiliations (c) Top corresponding authors' countries. SCP represents single-country publications and MCP, multi-country publications (d) Citation patterns over time.

The most productive institution is the University of California, San Francisco, with 173 articles, followed by the University of Washington and Johns Hopkins University, with 160 and 144 articles, respectively (Fig. 4b). It is interesting to find non-US institutions in the top 10 list, with King Saud University leading this cohort and followed by Cairo University, the University of Cape Town, and the University of Tehran Medical Sciences (Fig. 4b). These universities likely made the list due to increased investment in research, strong regional collaborations, advancements in specific scientific fields, and policies prioritising international visibility and high-impact publications [33,34]. It would be valuable for future research to investigate the specific factors enabling these institutions to lead in researching the connection between antimicrobial resistance (AMR) and mental health.

Fig. 4c shows the top corresponding author's countries. The United States had the highest collaboration frequency and the highest Multiple Country Publications (MCP). China, the United Kingdom, and Italy follow this. Collaboration was lowest for Poland, Denmark, Turkey, and Iran.

Fig. 4d shows an initial increase in total citations per article from 2004 to 2007. Fluctuations (year-to-year variations) were observed from 2007 to 2019, with a notable peak in 2017. The highest peak was recorded in 2020. The citation peak in 2020 may reflect the increased volume of publications during the height of the COVID-19 period, without implying a direct causal link. The observed decline post-2021 is likely due to factors such as the natural maturation of the field or the fact that papers published after 2021 have not yet had sufficient time to accumulate significant citation counts, a known limitation when analyzing very recent bibliometric data [35]. It might also be due to a normalization as the initial massive wave of COVID-19-related papers in psychiatry and infectious diseases subsides [36].

3.2. Collaboration patterns, research themes, and trends

Fig. 5a shows the collaboration network between institutions. A collaboration network is observed between Europe, North America, Asia, Africa, and Australia. Overall, there is a good cross-collaboration between countries. In Fig. 5b, keywords such as schizophrenia, pharmacogenetics, and depression stand out. Antimicrobial resistance, antibiotic resistance, and antibiotics in the same green cluster are associated with anxiety, depression, stress, and inflammation (blue cluster). The green cluster is also associated with the purple cluster containing antidepressants, psychosis, bipolar disorder, schizophrenia, treatment-resistant schizophrenia, electroconvulsive therapy, and antipsychotics. Studies suggest that antibiotics like minocycline and clarithromycin may have antidepressant effects, with some, like fluoroquinolones, having side effects such as anxiety and depression [37].

Fig. 5.

Fig. 5

Fig. 5

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(a) Institution collaboration network. Circles represent a research institution or organization. The circles are coloured and grouped into clusters based on their strongest collaborative relationships. The size of the circle represents a measure of the institution's productivity (total number of publications) or impact (total citations) within the analysed dataset. Lines represent a co-authorship relationship between two institutions. (b) Research keyword clusters. Circles represent a keyword. The size of the circle represents the frequency of the keyword, with larger circles indicating the most central and frequently studied keyword. Lines represent the co-occurrence or co-linkage between two keywords. Groups of similarly coloured circles represent distinct research clusters or sub-fields within the overall topic. (c) Keyword analysis showing the main themes and emerging trends. Circles represent a research cluster or theme. The size of the circle represents the volume or magnitude of the research output related to that theme. (d) Thematic evolution (e) Word cloud indicating topic relevance. The size of the word represents the frequency with which that keyword appears in the dataset. Larger words are the most central and frequently studied topics. The colour of the word is used for visual grouping to distinguish between different keywords.

In Fig. 5c, mental disorders such as depression, anxiety, bipolar disorder, and schizophrenia are contained in the motor theme quadrant. The motor theme is a well-developed research area fundamental to the research field. Mental health and antimicrobial resistance are in the quadrant for emerging or declining themes due to their low density and centrality to the research scope. This implies they are still gaining importance and have great potential for future research. Fig. 5d is a thematic evolution diagram representing the shift in research focus over two time periods: 2004–2016 and 2017–2025. The flow of themes illustrates how earlier research topics have evolved or connected to different topics in the later period. Depression continues to be a significant research theme across both periods, while schizophrenia appears to be less in the second time period. Both show a thin/low association with drug resistance, indicating that a more specific theme, like antimicrobial resistance, might receive even less attention for its association with mental disorders. This is further observed in Fig. 5d, which shows a word cloud representing the key themes. The larger words indicate topics with higher research prominence or frequency. Schizophrenia, depression, bipolar disorder, and anxiety are frequently occurring terms. Whereas antibiotic resistance is less frequent. We can, however, observe a larger word for drug resistance.

4. Discussion

The bibliometric analysis of 3449 documents highlights key trends and gaps in the intersecting fields of antimicrobial resistance and mental health. As no prior bibliometric studies have focused on the intersection of antimicrobial resistance and mental health, direct comparisons are unavailable. Still, we will discuss our findings within the context of our study. The consistent annual growth rate of 2.35 % demonstrates increasing research interest in these fields, particularly after 2017. The surge in publications from 2020 to 2021 could have been influenced by the COVID-19 pandemic. The pandemic may have acted as a catalyst, highlighting the possible association between infectious diseases, antimicrobial resistance, and mental health outcomes [38]. However, this is not a causal linkage, as causality was not assessed in this study. The subsequent decline in publications post-2021 suggests that while the pandemic temporarily boosted research activity, sustaining this momentum may require continued emphasis on the relevance of this intersection.

The dominance of journals like PLOS ONE and the strong presence of U.S.-based institutions, such as the University of California, San Francisco, and Johns Hopkins University, highlight this domain's leading sources and contributors. However, including non-U.S. institutions like King Saud University and Cairo University in the top 10 suggests a growing global interest and capability in researching these areas.

The United States leads in collaboration and citation impact, with a notable collaboration network between Europe and North America. However, lower collaboration levels in countries like Poland, Denmark, and Turkey indicate potential areas for strengthening international research partnerships. The United States has one of the highest prevalence of mental health disorders [39,40], and research shows that mental disorders are the highest in high-income North America [40,41]. However, it is essential to note that the low prevalence in areas such as sub-Saharan Africa and Asia may be due to the unavailability or inadequacy of epidemiological data [39,42]. While there is sufficient research and collaboration in the intersecting field, this must be reflected in authorship collaboration with international co-authorship at 14.53 %.

The keyword analysis reveals a focus on mental health issues such as schizophrenia, depression, and bipolar disorder, alongside AMR-related terms like antibiotic resistance, but with more focus on drug resistance in general. The clustering of these keywords suggests an emerging interdisciplinary focus, particularly on how AMR may influence mental health outcomes. The connection between AMR and mental health, though recognised, remains underdeveloped, as evidenced by their classification as emerging or declining themes in the thematic map. This underdevelopment highlights a critical gap in the research that requires a more robust investigation. Antimicrobial resistance causes the persistence of infectious bacteria, which in turn elicits the body's inflammatory response [43]. Inflammation in the brain, known as neuroinflammation, can affect neurotransmitter systems and may contribute to the severity of symptoms in mental health conditions. This neuroinflammation has been implicated in treatment-resistant schizophrenia and might also affect other mental disorders [[44], [45], [46]]. However, this relationship is still being researched, indicating that much remains to be understood about the biological mechanisms linking antimicrobial resistance and mental health outcomes.

The identification of mental health and antimicrobial resistance as emerging or declining themes, despite their relevance, highlights a critical gap in the research. This suggests that while the connection between AMR and mental health is recognised, it is still underdeveloped and requires a more robust investigation. Understanding these interactions will improve treatment strategies, especially for mental health patients with infections and antibiotic treatments. Given these emerging insights, future research could focus on several key areas. For instance, longitudinal studies could track mental health outcomes in patients with resistant infections to clarify whether antimicrobial resistance contributes to the onset or exacerbation of psychiatric conditions. Interdisciplinary collaborations between microbiologists, psychiatrists, and pharmacologists could lead to a deeper understanding of how antibiotic treatments interact with psychotropic medications, potentially influencing treatment outcomes. The findings from this study underscore the urgent need for public health policies that address the dual challenges of antimicrobial resistance and mental health. Policymakers should prioritize integrating mental health considerations into antimicrobial resistance strategies, particularly in the development of antibiotics and other treatments. Such strategies could be the WHO Global Action Plan on Antimicrobial Resistance (AMR), which focuses on health and hygiene [47], and the Comprehensive Mental Health Action Plan, which addresses mental health and well-being through leadership, integrated services, promotion, and research [48]. Public health initiatives should also promote responsible antibiotic use to prevent the rise of antimicrobial resistance, which could have far-reaching impacts on mental health outcomes. Enhanced global surveillance and cross-sector collaboration are crucial to effectively addressing these interconnected health challenges.

Dietary interventions such as probiotics and prebiotics could help mitigate antimicrobial resistance, support gut health, and enhance mental well-being by restoring microbial diversity, promoting beneficial bacteria, and regulating neurotransmitter production [[49], [50], [51], [52], [53], [54]]. The study by Berding et al., 2022, found that a psychobiotic diet, rich in prebiotic and fermented foods, reduced perceived stress and elicited specific metabolic changes in the gut microbiota, suggesting potential for dietary approaches to improve mental health [55]. According to the study by O'Riordan et al., 2025, the gut microbiota influences brain function and behaviour through immune system modulation, with probiotics and prebiotics demonstrating potential in mitigating neuroinflammation and improving psychiatric, neurodevelopmental, and neurodegenerative disorders via strain-specific effects (variations in outcomes caused by different strains of microorganisms) [56]. They highlighted that the Lactobacillus and Bifidobacterium strains alleviate depression, stress, and neurodevelopmental symptoms [56]. The study by Schneider et al., 2024 highlights that by modulating gut microbiota through the diet–microbiota-gut-brain axis, probiotics help combat antimicrobial resistance, restore gut health, and improve mental health, including mood and cognition. Strains like Lactobacillus rhamnosus (JB-1) reduce anxiety, regulate immune responses, and alleviate neuroinflammation, offering potential therapeutic benefits for neuropsychiatric disorders [57]. Several bibliometric studies have also highlighted the relationship between probiotics, gut health, and mental disorders. In studies by Xu et al., 2025 and Xu et al., 2024, abnormalities in gut microbiota were linked to depression and highlighted a trend toward microbiome-based interventions, such as probiotic therapies [58,59]. Guo et al., 2025 identified probiotics as a current research hotspot and detailed how these microbial therapies are being explored to treat anxiety disorders by regulating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin (5-HT) via the gut-brain axis [60].

Antimicrobial resistance-specific interventions, such as Antimicrobial Stewardship (AMS) and Infection Prevention and Control (IPC), reduce the incidence of severe infections and the overall burden of antimicrobial resistance by decreasing the infection burden [61,62]. AMS directly optimizes antibiotic use by ensuring the right drug, dose, and duration are used for a specific infection, thereby minimizing unnecessary exposure to broad-spectrum antibiotics and preserving the efficacy of existing drugs [61,62]. Effective IPC measures, such as hand hygiene, isolation precautions, and environmental cleaning, prevent the spread of both drug-susceptible and drug-resistant organisms between patients, healthcare workers, and surfaces [61]. Since severe infections, especially those requiring hospitalization, are strong predictors of mental health disorders (e.g., post-traumatic stress disorder, depression, anxiety) and can cause or exacerbate neuroinflammation, minimizing this risk is a crucial indirect benefit to psychiatric health [[63], [64], [65]]. In the study by Lin et al., 2024, “probiotic” and “gut-brain axis” were identified as top keywords and research hotspots, citing trials where probiotic supplementation significantly reduced psychiatric rehospitalizations and improved cognitive function in patients with bipolar disorder [66].

4.1. Study limitations and strengths

This study represents the first comprehensive bibliometric analysis of the intersection between antimicrobial resistance and mental health. However, several limitations should be acknowledged. The analysis was restricted to publications indexed in Scopus and Web of Science, which may exclude relevant studies from other databases. Only English-language publications were included, which may introduce potential language bias. Author name disambiguation challenges may have impacted the accuracy of attributing publications and citations to individual researchers. Additionally, self-citations were not assessed, which could potentially influence citation-based metrics, such as the H-index or total citations. While we cannot guarantee that all retrieved publications fully meet the search criteria, the large dataset of 3449 documents ensures a robust and representative overview of the field, which is adequate for a bibliometric study to provide comprehensive, reliable, and data-driven insights [67,68]. Given the interdisciplinary nature of this research area, which spans psychiatry, microbiology, pharmacology, and public health, a smaller dataset could overlook key subtopics or emerging low-frequency themes. Despite these limitations, the findings provide reliable, data-driven insights into overall trends, influential authors, and thematic developments within this field.

4.2. Conclusion

Antimicrobial resistance negatively impacts mental health, primarily by disrupting the gut microbiome, which affects the central nervous system. While research on the relationship between antimicrobial resistance and mental health is emerging, it remains underdeveloped due to the indirect nature of this linkage. Addressing this complex intersection requires a multidisciplinary approach involving microbiology, psychiatry, and public health. Future research should explore the role of the gut-brain axis in mediating these effects and investigate how antimicrobial resistance could exacerbate mental health conditions like depression, anxiety, and neurodegenerative diseases directly or indirectly. Additionally, enhancing international collaboration and expanding research in underrepresented regions are crucial for a comprehensive understanding of AMR's impact on mental health and for developing effective interventions. This study paves the way for more integrated, evidence-based interventions to tackle the dual challenge of antimicrobial resistance and mental health by highlighting key trends, research gaps, and collaborative networks.

Ethical statement

Not applicable because a bibliometric study uses only publicly accessible secondary data and does not involve human participants, identifiable personal information, or any intervention.

Data availability statement

The data generated are contained within the manuscript.

Author contributions

O.E: Conceptualisation, Methodology, Software, Formal analysis, Investigation, Project administration; Resources; Supervision; Validation, Visualisation, Writing - original draft; Writing - review & editing. O.Y.: Formal analysis, Investigation, Supervision, Validation, Writing - original draft, Writing - review & editing. C.D.: Formal analysis, Investigation, Supervision, Validation, Writing - original draft, Writing - review & editing. C.O.U.: Conceptualisation, Formal analysis, Investigation, Supervision, Validation, Writing - original draft, Writing - review & editing.

Funding

This study received no external funds.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors acknowledge the Poznan University of Medical Sciences for their support in promoting research and internationalization. O.E., C.D., and O.Y. are participants of the STER Internationalization of Doctoral School Program from NAWA, the Polish National Agency for Academic Exchange, No. PPI/STE/2020/1/00014/DEC/02.

Contributor Information

Cyril Onwuelazu Uteh, Email: utehcyril@gmail.com.

Ovinuchi Ejiohuo, Email: ovinuchi.ejiohuo@gmail.com.

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