The microbiome and non-occupational human-animal interactions: a scoping review

Abstract

Human-animal interactions may impact the composition of the human microbiome. This can then influence human physical, mental, and social wellbeing. This scoping review synthesizes the literature on the impacts of intentional, non-occupational human-animal interactions on the human microbiome. Six databases (PubMed, CINAHL, PsycINFO, Web of Science, CAB Abstracts, and ProQuest Dissertations and Theses) were searched using keywords for human-animal interactions and microbiomes. Database searching resulted in 2,946 publications. Publications were screened according to the following inclusion criteria: (1) published in English, (2) peer-reviewed article or dissertation presenting empirical data, (3) studies humans engaging in intentional, non-occupational interaction with animals, and (4) compares human microbiome composition before and after human-animal interactions or compares humans that interact with animals to humans that do not interact with animals. Screening resulted in 30 relevant publications. Of these, 24 reported a significant effect of human-animal interactions on microbiome composition. However, there were notable gaps in the reporting of demographics and methodology, such as length of human-animal interaction, species of animal, and variable 16S regions of focus. To better understand the impacts of human-animal interactions on the human microbiome, future studies should include detailed reporting of both human-animal interactions and microbiome analysis methods.

Introduction

There is a growing interest in the interconnectedness of people, animals, plants, and the environment, often referred to as One Health or Traditional Ecological Knowledge (1, 2). While this interconnectedness is often discussed in the context of zoonosis, the microbiome is beginning to be recognized as an important and potentially beneficial aspect of One Health (3). The human microbiome is the collective community of microorganisms inhabiting a specific human tissue (e.g., gut, skin, mucous membrane) (4). The composition of human microbiomes may influence the physical, mental, and social wellbeing of their host (4, 5). This composition can be impacted by factors external to the host, such as interactions with other people, animals, and the environment (1, 6, 7). Understanding the impacts of these interactions on the composition of human microbiomes may provide a deeper understanding of the interconnected mechanisms of human wellbeing.

Of particular interest are the impacts of interactions between humans and animals on the human microbiome. Humans seem innately drawn to animals and may intentionally engage in human-animal interactions (HAIs) (8). Interacting with a companion animal or pet is one of the most common forms of HAI, with an estimated 60% of households containing at least one companion animal (9). However, HAIs take many forms, including, but not limited to companion animals, animal-assisted therapy, animal-assisted education and humane education, employment that involves working with animals, and human-wildlife interactions (10). Given the scope of HAIs and the potential impacts of the microbiome on human health, it is important to understand the relationship between HAIs and the human microbiome.

Microbiome and human health

Microbiomes have the potential to impact the health of their human host (4, 5). Numerous public health concerns including obesity, diabetes, cancer, food allergies, depression, and Alzheimer's Disease have been linked to microbiome composition, particularly the gut microbiome (4). Microbiome composition can be influenced by environmental factors such as animal exposure, antibiotic use, sexual activity, smoking, dietary choices, socioeconomic environment, and built environment (6, 11). Continuous particle shedding during human and animal activities leads to microbial exchange through vectors like dust, air, water, food, and physical touch (4, 12–14). Due to the vast number of environmental influences on the microbiome, it is important to understand how these changes impact human health.

Some environmental conditions that impact microbiome composition have been characterized in the current literature. Residents of urban environments display lower microbial biodiversity than those in rural environments (15). This difference can lead to immature immune systems and delayed organ development for urban residents (15). In rural environments, farmers have shown differences in microbiome composition based on which livestock they work with (12). Additionally, the length of interaction with livestock impacts the magnitude of the difference (12). Early life exposure to microbes on farms correlates to a decreased likelihood of developing diseases such as atopy and asthma (11).

As with farmers and livestock, humans can exchange microbiota with their companion animals, impacting both the human's and animal's health (1). Living with a dog or cat has been linked to greater biodiversity within the microbiome (1). However, the type and amount of exchanged microbes vary significantly depending on environmental factors and which microbiomes are studied (1). On the contrary, some studies have found mixed results or no significant changes due to interactions with companion animals, showcasing inconsistency in the current literature (16–18). With risks of confounding environmental variables, small sample sizes, and a lack of agreement on what comprises a “healthy microbiome,” it is difficult to understand the extent of the impact that interacting with animals has on the human microbiome (1). These conflicting studies highlight a need to synthesize the literature to identify avenues for further understanding the impacts that HAIs have on the human microbiome.

Human-animal interactions

Interactions between people and animals range from encounters with wildlife to cohabitating with companion animals (10). However, recurring, long-term interactions with animals are of particular interest for understanding the impact of HAIs on the microbiome due to correlations between the length of HAIs and the impact on microbiome composition (1). These recurring interactions would include interactions such as with companion animals, service animals, or animals involved in animal-assisted education (e.g., classroom pets).

Companion animals are commonly referred to as pets and include dogs, cats, birds, fish, rabbits, reptiles, and small rodents (19). Service animals, sometimes known as assistance animals, are animals that perform specific tasks to help one person manage their disability (20). In the United States, service animals must be dogs or miniature horses (20). They often undergo extensive training to partner with a handler and perform needed support tasks for vision, mobility, or psychiatric support (10). Animal-assisted education involves incorporating animals into formal or informal educational programs (21). This can take many forms, such as incorporating pets into classrooms, participating in humane education courses at an animal shelter, or reading to a dog at a library (21).

Interacting with animals can improve human psychological, physiological, and social wellbeing (10). Socially, HAIs encourage human-to-human interaction and increase the feeling of being part of a community (10, 22). In particular, those with service dogs report being greeted and approached more in public than those without service dogs, combating feelings of loneliness and isolation (22, 23). Reported psychological benefits include alleviating loneliness, improving feelings of self-worth, and improving symptoms of psychiatric disorders (10, 24). Improvements in executive functioning, attention, and motivation may also be correlated with HAIs (10). Physiological health benefits include fewer doctor visits, higher survival rates from coronary artery disease, more frequent exercise, and decreased blood pressure (25, 26). Despite these benefits, HAIs may also negatively impact human wellbeing. Common negative impacts include allergies, zoonosis, bite risk, caregiver burden, housing challenges, and financial impacts (27, 28). Ultimately, research indicates that for many people, the benefits of HAIs outweigh the costs and can directly yield physical, social, and psychological benefits (25, 29).

In addition to these direct impacts, there is increasing evidence of the potential for HAIs to indirectly impact human wellbeing. Animals have the capacity to influence the environmental microbiome by introducing new beneficial bacteria to the environment, which can in turn influence the human microbiome (12, 13). Living with animals, such as on farms or with companion animals, leads to protective measures within the microbiome that may help reduce the risk of food allergies, atopy, asthma, and antibiotic resistance (30–32). These impacts on human health underscore the need to examine the current literature on HAIs' effects on the microbiome.

Current review

Considering the impact that microbiome changes can have on human physical and mental health, it is crucial to understand how HAIs may impact the human microbiome. The primary focus of this scoping review was to summarize and synthesize literature focused on how human microbiomes may or may not change in the context of human-animal interactions. Given that humans and animals interact in a multitude of different ways on a daily basis, it was important to limit the scope of examined HAIs to comparable forms of interaction. This allows current evidence to be more accurately compared and synthesized. Consequently, the research question for this review was “What are the effects of intentional, non-occupational human-animal interactions on human microbiomes?” Intentional interactions include therapy animal visits, companion animal interactions, humane education, animal-assisted education, service dog partnerships, and volunteer work (21). Interactions due to the human's employment were excluded due to the potential for major differences between occupational and non-occupational interactions (e.g., condition of animal, length of interaction, type of animal, method of interaction, etc.). Narrowing the target of this scoping review to intentional, non-occupational HAI allowed for a more focused investigation of comparable literature.

A scoping review was chosen over a systematic review as scoping reviews are more appropriate for emerging topics, as they can more broadly identify the way research on the topic is being conducted (33). A formal, standardized assessment of quality, bias, or methodological limitations was not conducted, as these are not standard analyses for scoping reviews (33). In alignment with the purpose of scoping reviews, aims were developed that serve to clarify the scope of the current literature, identify gaps, and provide an overview of findings. The first aim of this review (Aim I) was to describe the composition of the literature related to intentional human-animal interaction and the microbiome. The second aim (Aim II) was to summarize the human-animal interactions being measured and the methods of measuring the microbiome. The third aim (Aim III) was to summarize the reported outcomes related to human-animal interaction and the microbiome.

Methods

Search strategy

A scoping literature review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Scoping Reviews (PRISMA-ScR) guidelines (34). The PRISMA flow chart in Figure 1 summarizes the article collection and screening process. Prior to conducting the literature search, all study procedures were established, including the search strategy, inclusion and exclusion criteria, and the data extraction procedure. This protocol was preregistered with OSF (Registration https://doi.org/10.17605/OSF.IO/CN6TM). A total of six databases were searched from the inception date to 21 February 2025 (PubMed, CINAHL, PsycINFO, Web of Science, CAB Abstracts, and ProQuest Dissertations and Theses). The search strategy was adapted to each database by mapping terms appropriately to each database's vocabulary, in consultation with an academic librarian. All database searches included identifiers for human-animal interactions (e.g., “companion animal”, “therapy horse”, “service dog”) and identifiers for human microbiomes (e.g., “microbiome”, “gut flora”, “microbiota”). The full list of identifiers is available in Supplementary materials. The database search resulted in 4,240 articles.

Figure 1

Criteria and screening

After manual removal of duplicates in Zotero (n = 1,295), all remaining articles were uploaded to Covidence (35). Additional duplicate entries were automatically removed by Covidence (n = 11), resulting in a total of 2,946 articles that moved on to the screening process. See Table 1 for inclusion and exclusion criteria.

The research team, consisting of four people, first completed a round of title and abstract screening in which articles deemed irrelevant based on the title and abstract were removed. Once title and abstract screening concluded, the full texts of the remaining articles (n = 45) were reviewed for relevance, using the criteria outlined in Table 1, by two members of the research team. In each round of screening, two members of the research team reviewed each title and abstract or full text, respectively, and voted “Include”, “Exclude”, or “Maybe”. Conflicts occurred when the two people disagreed in their votes. Once a week the research team met for a conflict resolution meeting in which they discussed the conflicts and came to a conclusion as a team on how to vote. After screening was completed, the resulting articles (n = 29) were included in the scoping review. Additionally, backward and forward citation checking was conducted by one team member to capture any articles that were missed by the keyword search. This resulted in one additional article being included (36). While this article was published after the database search date, it was deemed appropriate to include because it was published only shortly after the database search and because inclusion would more accurately represent the breadth of literature on HAI and human microbiome composition.

Data extraction

Following screening and citation checking, 30 articles remained that aligned with the inclusion criteria and explored the influence of human-animal interactions on human microbiomes. A pre-registered data extraction template was created prior to data extraction and loaded into Covidence, where it was used to extract data from each article. See Supplementary materials for the data extraction template. Two members of the research team collaborated to extract data. Data extracted for Aim I focused on the composition of the current literature and documented the research aim or hypothesis of the article, the title, name and country of the authors, year of publication, journal of publication, and demographic information on both human and animal participants. Data extracted for Aim II looked at the measures for HAI and the microbiome and included additional demographic information for human and animal participants, sample size, details on how samples were collected, handled, processed, and analyzed, study design and limitations, ethics approval, and information about the microbes present in both conditions. Data extracted for Aim III summarized the reported outcomes of the studies, including noting if there were differences in the microbiome between HAI and no HAI and the general outcomes. Once the data were extracted from all articles, one member of the research team reviewed and finalized the extracted data. Any discrepancies in extracted data were discussed with the team until consensus was reached.

Results

The results of data extraction are reported in line with Aim I, Aim II, and Aim III.

Aim I: composition of literature

Table 2 provides a list of the 30 included publications and a summary of their composition. One publication was a dissertation chapter (37), while all other publications were peer-reviewed journal articles (n = 29). The earliest year of publication was 2013 (38–40), while the most recent was 2025 (36, 41). Sixty percent (n = 18) of the publications were published within the last 5 years prior to the database search (6, 16, 18, 36, 37, 41–53). Publications were from authors in 15 different countries; these were the United States (n = 7) (16, 18, 37, 40, 43, 54, 55), Canada (n = 4) (38, 56–58), China, (n = 3) (6, 45, 48), Sweden (n = 3) (51, 53, 59), Finland (n = 2) (39, 46), Germany (n = 2) (17, 50), Australia (n = 1) (36), Czech Republic (n = 1) (41), Denmark (n = 1) (60), Ireland (n = 1) (49), Italy (n = 1) (52), Japan (n = 1) (47), South Korea (n = 1) (44), Spain (n = 1) (42), and The Netherlands (n = 1) (61). Thirteen publications explicitly stated their study design (16–18, 36–38, 44, 46, 49, 51, 53, 59, 61), 14 did not state their study design (6, 40–43, 45, 47, 48, 50, 52, 56–58, 60), and three publications, which consisted of sub-studies, described the design of their parent studies (39, 54, 55). Publications often utilized multiple study designs for the same study. The utilized study designs were longitudinal (n = 13) (18, 36, 37, 43, 46, 47, 49, 52, 53, 55, 58–60), cohort (n = 12) (38, 43, 47, 49–52, 54, 56, 58–60), cross-sectional (n = 10) (6, 17, 40–42, 44, 50, 56, 57, 59), observational (n = 9) (36, 38, 41–43, 47, 49, 58, 61), case-control (n = 4) (16, 45, 46, 48), nested (n = 2) (16, 55), randomized controlled trial (n = 2) (39, 46), prospective (n = 1) (49), and non-randomized experimental (n = 1) (18). Total sample sizes ranged widely [range = 10–746, mean (M) = 144, median (Mdn) = 85].

Aim II: HAI characteristics and microbiome characteristics

Table 3 displays the demographics for the human participants in the included publications and Table 4 displays the demographics for the animal participants. Table 5 displays the characteristics of the studied HAIs in the included publications. Table 6 displays the characteristics of the studied microbiome in the included publications.

Human demographics

Twenty-eight publications provided age demographics for human participants. Of these, 18 focused exclusively on infants or children (6, 17, 18, 36, 38, 39, 43, 46, 49, 51–54, 56, 58–61), seven focused exclusively on adults (16, 42, 45, 47, 48, 50, 57), and three included both adults and children (40, 44, 55). Twenty-three publications provided gender demographics for human participants. Of these, two publications included exclusively men (37, 50), while the other 21 included multiple genders (6, 16–18, 36, 38–40, 42–45, 47–49, 51, 53, 55, 57, 59, 61). Eight publications provided race demographics (16, 18, 36, 45, 49, 54, 57, 58) and five publications provided ethnicity demographics (41, 45, 49, 52, 60).

Animal demographics

Animal demographics were more sparsely described than human demographics. Twenty-five articles described the species involved in the HAIs. Of these, the most commonly examined species were dogs (n = 21) (16, 18, 38–44, 46–48, 51, 53–60) and cats (n = 16) (16, 38–40, 42, 44, 45, 51, 53–60). Four studies described the species using terms such as “including cows,” “mainly cats and dogs,” and “other furry pets,” indicating there were likely other included species that were not provided in the publication (17, 39, 42, 58). Three studies provided information on the breed of the included dogs (40, 46, 47). Four studies provided information on the age ranges of studied animals, specifically three with information on dogs (43, 46, 47) and one with information on both dogs and cats (55). The types of animals studied were companion animals (n = 25) (6, 16, 18, 36, 38–42, 44–49, 51, 53–61), farm animals (n = 3) (17, 37, 52), therapy animals (n = 1) (43), and one study that included both companion and farm animals (50).

Human-Animal interaction characteristics

Some HAI characteristics were also sparsely described, with only six publications describing the number of animals involved in the interaction (16, 42, 44, 47, 55, 57) and only eight publications describing how much time was spent interacting with the animals (18, 37, 43, 44, 47, 50, 52, 54). Nearly all publications (n = 27) gave sample sizes for both the HAI group (range = 8–409, M = 67) and the control group (range = 8–337, M = 63) (6, 16–18, 37–48, 50–59, 61). However, one study combined participants exposed to animals and participants exposed to plants, so it is unclear how many participants were exposed to animals specifically (6). Another study included participants with occasional contact with animals in their control group (50). The types of HAIs studied were interacting with companion animals (n = 25) (6, 16, 18, 36, 38–42, 44–49, 51, 53–61), living on a farm (n = 1) (17), interning on a farm (n = 1) (37), visiting an educational farm for humane education programming (n = 1) (52), hospital therapy animal visits (n = 1) (43), and one study that included both living on a farm and interacting with companion animals (50).

Microbiome characteristics

The human microbiomes measured in the included publications were fecal (n = 23) (6, 16, 18, 37–42, 44–49, 51–54, 56, 58, 60, 61), oral (n = 3) (36, 40, 59), skin (n = 3) (40, 55, 57), nasal (n = 2) (17, 43), and saliva (n = 2) (50, 53). Nearly all publications measured only one sample microbiome type, but one study examined both fecal and saliva microbiomes (53), and one study examined fecal, oral, and skin microbiomes (40). Most studies utilized 16s rRNA sequencing (n = 27) (6, 16–18, 36–38, 40–50, 52–60), while two studies utilized qPCR (39, 61), and one study utilized quantitative culture (51). Of the studies utilizing 16s rRNA sequencing, eighteen used solely operational taxonomic units (OTUs) clustering (16–18, 36, 38, 40, 42, 44, 45, 48, 50, 53–59), two used solely Amplicon Sequence Variants (ASVs) (46, 47), two used both OTU clustering and ASVs (6, 43), one used both OTU clustering and whole-metagenome shotgun sequencing (37), one used a k-mer-based approach (41), and one used ASVs, whole-metagenome shotgun sequencing, and metagenome assembled genomes broken down into Species-Level Genome Bins (SGBs) (52). Two studies did not state their approach (49, 60). Most publications included the V3 and/or the V4 hypervariable regions in their analysis (n = 20) (16–18, 37, 41–44, 46, 47, 49, 50, 52–55, 57–60).

Aim III: outcomes on microbiome due to human-animal interactions

Table 7 displays the impacts of HAIs on human microbiomes found in the studies. Twenty-four studies reported a statistically significant effect of HAI on microbiome composition (6, 18, 36–40, 43–46, 48–55, 57–61), though two noted that significance varied depending on the measure used (18, 43). Of these twenty-four studies, significant differences included either more diversity in the microbiomes of the HAI group (n = 9) (6, 18, 38, 40, 43, 52, 55, 57, 58), or differences in microbiome composition between groups with no difference in their diversity (n = 14) (36, 37, 39, 44–46, 48–50, 53, 54, 59–61). One of the statistically significant studies found significant differences in the timing and frequency of microbial colonization between groups (51). Four studies did not find statistically significant differences between groups (16, 17, 41, 47). Two studies noted differences in microbial composition between groups, but significance levels are unclear (42, 56).

Discussion

The overarching goal of this scoping review was to collate literature to understand the effects of intentional, non-occupational human-animal interactions on human microbiomes. Databases were searched from inception, which included over five decades of publications. However, over half of the publications included in this review were published within the last five years. Additionally, multiple empirical studies on this topic have been published after the date of the database search for this review (7, 62, 63), indicating increased interest in this topic. Recognizing this trend, Skoufos et al. (1) called for a holistic understanding of human microbiomes within the context of the One Health approach.

This scoping review contributes to this understanding and draws on current literature to provide implications and future directions. The objectives included describing the composition of the literature related to intentional human-animal interaction and the microbiome, summarizing the human-animal interactions being measured and the method of measuring the microbiome, and summarizing the reported outcomes related to human-animal interaction and the microbiome. Findings of this scoping review demonstrate the potential for HAIs to influence the human microbiome.

Aim I: composition of literature

Aim I of this scoping review was to describe the composition of the literature related to intentional human-animal interaction and the microbiome. Findings indicated that interest in this topic has grown quickly in recent years, with over half of the reviewed articles published in the last five years. This rapid, recent growth is seen in literature on other topics in both HAI (64–66) and the microbiome (67, 68). Interest in this topic is also international, as evidenced by the authors of these articles representing fifteen different countries. Compared to many other recent HAI or microbiome reviews, this review represents articles from a much higher number of countries, even when other reviews look at more articles (24, 66, 69, 70). This rapid and diverse growth coincides with the increase in recognition of the importance of One Health across the globe and the bidirectional impacts of HAIs. The increase in studies specifically focused on the impact of HAIs on the microbiome reflects interest in One Health at the intersection of microbiology and macro-level interactions.

Study designs were highly varied, with the most commonly stated being longitudinal, cohort, and cross-sectional designs. In line with other HAI and microbiome studies, sample sizes were also highly varied, with a drastic difference between the smallest sample size, 10, and the largest sample size, 746 (66, 68–70). However, overall sample sizes per study tended to be <200, with ten studies having an overall sample size of <50. These smaller sample sizes are particularly notable, as both microbiome and HAI studies have been criticized for having small samples that may impact reliability (66, 70). It may be that these smaller sample sizes are reflective of the niche nature of many of the populations of interest, such as veterinary students at a farm (37) or pediatric oncology patients engaging with visitation dogs at a single facility (43). Indeed, the largest samples in this review are from articles that looked broadly at companion animals and that acted as nested or secondary studies that had access to large cohorts or data sets (16, 45, 49, 58). Some populations represent smaller proportions of the overall population, and are more challenging to recruit, thus obtaining large sample sizes may be impractical. However, alpha and beta diversity index results may be heavily influenced by sample size (71). Therefore, careful attention must be paid to which diversity index is used and how those results are reported.

Aim II: HAI characteristics and microbiome characteristics

Aim II of this scoping review was to summarize the human-animal interactions being measured and the methods of measuring the microbiome. Findings for this aim demonstrated that key information on human demographics, animal demographics, and HAI characteristics were often not reported, or reported unclearly. Less than one third of the reviewed articles reported race or ethnicity. Considering that ethnicity has been tied to measurable variations in the microbiome, documenting the ethnicity of participants is important for a full understanding of microbiome composition changes (72). Documenting ethnicity seems even more salient considering the diversity of countries represented in this review.

While most articles reported human age data, the way that age was reported varied greatly. Some articles reported detailed statistics, some articles broke ages down by study group, and some reported only high-level information such as the age range of the full sample. Human demographics, such as age, impact both the composition of the microbiome, as well as its susceptibility to change (4, 73). Thus, this inconsistency in reporting demographics impacts not only a full understanding of an individual article's findings, but also hinders comparison across studies.

Animal demographics were reported even less often. The age range of the animals were provided in only four articles, and the breeds of animals were provided in only three articles. As with human microbiomes, age impacts composition of and susceptibility to change in animal microbiomes (74). With the variation in age reporting for the human samples and the lack of age reporting for animal samples, not only is it unclear how age impacts each group's microbiome composition separately, the complex interactions between human and animal microbiomes are made even more complicated to interpret. Additionally, while most articles reported species information, some articles used vague statements such as “mainly cats and dogs” or “other furry pets.” Details of the HAIs, such as number of animals and length of time spent with animals, were similarly sparsely reported. HAI studies have previously been criticized for high variability and unclear reporting, which make comparison between studies difficult (75). To ensure studies of HAI and microbiomes do not face similar concerns, efforts should be taken to thoroughly and accurately report human demographics, animal demographics, and HAI characteristics.

Aim III: outcomes on microbiome due to human-animal interactions

Aim III of this scoping review was to summarize the reported outcomes related to human-animal interaction and the microbiome. The majority of included publications demonstrated a difference in human microbiomes, either between groups, or before and after interacting with animals. While the character of differences in microbiomes varied between publications (e.g., microbial diversity, microbiome community composition, timing and frequency), the consistent presence of changes is notable.

More specifically, the reported effects of HAI were heterogeneous in both nature and direction. Nine studies reported greater microbial diversity associated with HAI, whereas fourteen identified differences in microbial community composition without corresponding differences in diversity. One study identified differences in the timing and frequency of microbial colonization. Conversely, four studies reported no statistically significant differences, and two reported compositional differences for which statistical significance was unclear. Thus, the available literature more consistently supports an association between HAI and differences in microbiome composition than a uniform directional effect on microbial diversity. Importantly, the heterogeneity of study populations, HAI exposures, microbiome sites, sequencing and analytical approaches, and reported outcomes prevents meaningful comparison of effect magnitude across studies. Likewise, changes in microbial diversity or taxonomic composition should not, in themselves, be interpreted as clinically beneficial or detrimental without corresponding functional or health outcomes. The null findings are therefore also informative and underscore that microbiome responses to HAI are likely context-dependent rather than universal.

Implications

It may be possible that HAIs, which are known to have direct biopsychosocial impacts on humans, are also producing indirect impacts via changes to the microbiome (25). Considering the known connection between the composition of the microbiome and human physical, mental, and behavioral health, it is imperative to understand this potential indirect pathway for the impact of HAIs (5, 76, 77). Evaluation of the microbiome can be incorporated into future studies of HAIs to create a fuller picture of their biopsychosocial impacts, and consideration of potential HAIs can be incorporated into future studies of human microbiomes to better understand their composition. Additionally, analysis of HAIs and the microbiome may aid clinicians, practitioners, and public health professionals in supporting their clients.

Well-designed evaluation of the microbiome may serve as a novel approach to developing a more robust understanding of the biopsychosocial health impacts of HAIs. However, there are some notable gaps in the current literature that should be addressed to develop a better understanding of the impacts of HAIs on the human microbiome. In general, publications were heavily focused on the microbial aspects of the analysis, while documentation of the human demographics, details of the HAIs, and information on the animals participating in the interactions was sporadic. This missing information hinders a full understanding of these findings.

Limitations

Findings from this scoping review should be interpreted in the context of the following limitations. Given the diverse nature of HAIs, it was necessary to narrow the scope of this review to the impact of intentional, non-occupational HAIs on human microbiomes to ensure feasibility and cohesion. However, this resulted in exclusion of studies covering topics such as farm workers, veterinary staff, human-wildlife interactions, and impacts on animal microbiomes. In addition, the variety of interventions and study types necessitated that this review would be a scoping review in format. Consequently, there was no formal quantification of methodological rigor or risk of bias. Future studies would benefit from a thorough review of other types of HAIs, a review of animal microbiomes, and a systematic analysis of existing literature.

Additionally, the search strings resulted in a relatively large number of articles for manual review, which may have increased the risk of mis-categorization of articles. This was generally due to HAI terms also serving as acronyms in many fields, such as “pet” also serving as an acronym for “positron emission tomography” and “polyethylene terephthalate.” To minimize this risk, a research librarian was consulted for refinement of search strings in accordance with each database's search functions. Additionally, multiple team members reviewed each article, and disagreements were addressed as a group.

While the data extraction process was a collaborative effort between multiple researchers, the backwards and forwards citation checking was conducted by one team member. There is potential for this process to introduce bias in excluded or included articles. However, this team member was a key part of the data extraction process, and thus highly familiar with the already included articles and the inclusion/exclusion criteria. Additionally, the one article chosen for inclusion based on citation checking was approved by additional team members. Therefore, while there is potential for articles to be excluded erroneously, it is unlikely that the included article should have been excluded instead.

The lack of sufficient explanation of the methodology used in microbiome sequencing limits the comparability of these studies. While this scoping review lists 16S, OTU/ASV, WMS, qPCR, and culture approaches, other important details were lacking in most cases. Information on OTU vs. ASV pipelines, variable 16S regions, DNA extraction, sequencing depth, batch effects, statistical pipelines, and taxonomic databases can all change conclusions.

Future directions

Future studies of the impact of HAIs on the microbiome should aim to involve an interdisciplinary team of collaborators that includes subject-matter experts (SMEs) on the microbiome, human-animal interactions, and human health. A thoughtfully composed team can design studies and report findings in a way that holistically integrates all three of these topics, ensuring generalizability and clinical usefulness. In particular, studies of biopsychosocial impacts of HAI should incorporate microbiome SMEs to ensure that they are getting a full picture of HAI impacts. Studies of human microbiomes should incorporate HAI SMEs to ensure clear reporting of animal and HAI information.

Additionally, future studies should consider expanding which HAIs and microbiomes are explored. Existing literature is focused primarily on human-companion animal interactions, with only minimal focus on other forms of intentional, non-occupational HAI. Studies should examine HAI types including service dogs, therapy animals, volunteer-based interactions, and educational interactions. Existing literature also primarily explored the fecal microbiome. This is likely a practical choice based on the ease and standardization of fecal microbiome sampling. However, this review included studies of non-fecal microbiomes with findings indicating significant impacts of HAI. This indicates that impacts on non-fecal human microbiomes are particularly under-explored and need to be further understood. Future microbiome studies should adhere to acceptable guidelines for reporting their results. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) reporting guidelines, or an equivalent, be included in Supplementary materials of future microbiome studies (78).

Conclusion

This scoping review provides a timely contribution to understanding the impacts of HAIs on the human microbiome. This understanding can provide a more complete picture of human biopsychosocial wellbeing within a One Health perspective. Findings from this scoping review are relevant to both practitioners and researchers. These findings can contribute to research via supporting thorough, well-executed study designs. Additionally, these findings provide initial evidence that microbiome analysis may serve as a novel approach for practitioners to understand and support the wellbeing of their clients in a holistic way. Interest in this topic has grown significantly over the last five years and is expected to continue growing. In response, this scoping review illuminates the current literature on HAIs and the human microbiome, and lays a foundation for future research.

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