1 Introduction
The relationship between dietary patterns and the microbiome has surfaced as a critical
factor in management of human health, including prevention and treatment of diseases
including obesity [[1]
[2]
[3]], type 2 diabetes (T2D) [[2], [4]], Inflammatory Bowel Disease (IBD) [[5], [6]], and more. As research moves towards precision nutrition as a means to mitigate
increasing rates of diet-driven disease, many challenges have come into view. Diet
is not the only factor influencing rising rates of obesity and T2D. Recent literature
suggests that factors such as sleep patterns [[7]], access to food [[8]], and time available for food preparation in the home [[9]] also play a role, for example.
Consumers in the United States face challenges when providing a nutritious diet for
themselves and their households, including, but not limited to, low nutrition literacy,
food costs, food waste, supply chain shortages, lack of time to prepare meals, and
food access. These challenges translate to increased risk for personal health problems
as well as increased burden on the healthcare system over time, with diet-driven disease
accounting for an estimated 20% of healthcare costs in the United States [[10]].
Consumers are largely responsible for the management of nutrition and dietary choices
in their own lives and households. Even when access to health care providers is available,
most primary care physicians have limited time to discuss nutrition with their patients
in depth [[11]]. Existing nutrition research has reinforced the importance of consumer understanding
of food composition, preparation, access, and dietary behaviors for prevention, management,
and treatment of diet-driven diseases [[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]]. To this end, there are several digital health applications designed to support
the consumer in making nutritious food choices and preparing nutritious meals for
themselves and their households. The effectiveness of a digital application is tied
not only to its accuracy and quality, but also the application’s ability to engage
a consumer in a consistent manner [[21], [22]]. Therefore, the impacts of digital health interventions aimed at behavioral modifications
in diet, physical activity, sleep, or wellness are directly tied to consumer adoption
and consistent use [[23], [24]]. The research described in this brief year-in-review survey focuses on the design
and development of tools for self-management in an outpatient or community setting.
The research highlighted recognizes both the challenges of and needs for self-management
tools that support consumer use and engagement, as well as enhanced rigor when understanding
mechanisms that influence our diet, such as the microbiome. This survey aims to provide
a means for acknowledgement and understanding of the breadth of relationships and
knowledge needed to address diet-related disease from an interdisciplinary informatics
perspective.
The diet, the microbiome, and consumer behavior are intrinsically linked. The aim
of this survey was to synthesize recent literature describing how technology is being
applied to understand health at the interface of nutrition and the microbiome, with
a special focus on the perspective of the consumer, as shown in [Figure 1].
Fig. 1 A high-level overview of themes and topics covered in this survey. The concept of
dietary patterns, impact on the gut microbiome, and impact of literature reviewed
on precision medicine and nutrition is inclusive of multiple disciplines, including
health informatics and bioinformatics.
2 Methods
A survey of the literature published between January 1, 2021, and October 10, 2022
was performed using the PubMed database. The search was performed exactly as written
below using the exact query provided:
(“nutrition” AND “microbiome” AND “informatics”) OR
(“consumer health informatics” AND “nutrition”) OR
(“consumer health” AND “informatics” AND “nutrition”) OR
(“user experience” AND “nutrition”) OR
(“accessibility” AND “nutrition” AND “microbiome”) OR
(“health informatics” AND “microbiome”) OR
(“consumer health” AND “microbiome”)
All results from the search above were downloaded as a comma-separated values (csv)
file; after checking for duplicate articles, a total of 139 total papers were found.
Inclusion and exclusion criteria ([Table 1]) were formed around identifying recent literature that focused on the intersection
of diet and the microbiome from the perspective of a consumer or a patient managing
diet-driven disease at home. For example, diet-driven interventions that were self-managed
applied in a patient population would be included, but diet-driven interventions applied
to a patient population requiring majority clinical involvement or surgical interventions
were excluded. Only studies describing human data in whole or in part (i.e., studies
including data from mouse and human) were included.
Table 1 Inclusion and exclusion criteria used. A table describing the inclusion (top) and
exclusion (bottom) criteria used when examining the literature included in this manuscript.
[Table 1]. Inclusion and exclusion criteria used. A table describing the inclusion (top) and
exclusion (bottom) criteria used when examining the literature included in this manuscript.
Articles were screened against these inclusion and exclusion criteria. A total of
45 papers were included in the final survey. A total of 39 of the 45 papers (86.7%)
included in the final survey were available freely on PubMed Central and the remaining
papers were access through institutional access or interlibrary loan. This survey
does not reflect a comprehensive review of the literature but aims to identify emerging
themes and trends published in PubMed over the past year on this topic. The citations
for the 45 papers and their major theme classifications are listed in [Table 2], below. [Table 2] reports major themes found in the survey, composition, and references for papers
examined for each theme.
Table 2 Survey Themes.
In this context, “usability” refers to the ability of a product to be used by a consumer.
A more thorough explanation on usability is well defined by the International Organization
for Standardization (ISO) [[68]].
3 Results
A total of four major themes were identified in the literature and synthesized by
KC, each with its own emerging subthemes: (1) Microbiome and Diet-driven Disease,
(2) Usability and Accessibility of Consumer Health Tools, (3) Reproducibility and
Rigor of Computational Analysis in the Microbiome, and (4) Precision Medicine and
Precision Nutrition.
3.1 Microbiome and Diet-driven Disease
Microbiome. With decreasing costs of microbiome sequencing as well as increased interest in the
impact of the microbiome on health, the literature and data availability on this topic
is expected to grow. The literature in this review circled around diet-driven disease
with major public health implications: obesity/overweight, T2D, COVID-19, Irritable
Bowel Syndrome (IBS), Irritable Bowel Disease (IBD), and food allergies/intolerance,
among others. The literature on the microbiome and diet also focused on prenatal health
(gestational diabetes) and infant health. Notable subthemes observed in this research
include (1) continued interest in the impact of singular, short-term interventions
on microbiome composition, (2) proliferation of smaller studies on the microbiome
in diet-related disease, and (3) implicit need for reliable aggregation and analysis
of microbiome data to ensure replicability and rigor when applied in a larger human
population.
Diet-driven Disease. Most studies described in this survey capture data in a post-COVID world. It is expected
that the COVID-19 pandemic has influenced the gut microbiome due to rapid changes
in physical activity, diet, access to food, and exposure to one’s community [[39]] via mechanisms such as lockdowns, social distancing, isolation protocols, and quarantines.
One retrospective study of 3,055 16s rRNA microbiome datasets across 12 countries
aimed to find any population level changes associated with the COVID-19 pandemic [[27]]. Authors separated microbiome data into two groups: countries with higher COVID-19
hospitalization rates and countries with lower COVID-19 hospitalization rates [[27]]. Diversity in bacterial abundance (measured by Shannon’s alpha) was higher in countries
with “high” COVID-19 hospitalizations; this difference was statistically significant
[[27]]. It is possible to speculate on reasons why a relationship between microbiome diversity
and COVID-19 hospitalization might be found. Research outside this survey found evidence
that hospitalization rates vary due to differences in diet, physical activity, alcohol
and tobacco use, and other behaviors [[69]
[70]
[71]], although this evidence may be conflicting.
Irritable Bowel Syndrome (IBS). The potential impact of supporting patients living with gastrointestinal disease
is large – for example, an expected 25-45 million individuals suffer from Irritable
Bowel Syndrome (IBS) in the United States [[72]]. Four studies examined means to reduce symptoms for individuals living with IBS
through dietary management. In one study an oral probiotic was trialed (n=15 adults) with IBS over a period of 4 or 8 weeks [[30]]. The probiotic, called VSL#3®, contains bacteria from the genus Lactobacillus, Bifidobacterium, and Streptococcus [[73]]. Microbiome composition studies from before and after the study period detected
bacteria from all three genera in the group treated with the probiotic, but found
no difference in abundance before and after treatment [[30]]. Despite this, participants in the probiotic group reported reduction in pain and
symptoms [[30]]. Another study of the gut microbiome collected from n=34 individuals diagnosed with IBS and receiving Cognitive Behavioral Therapy (CBT)
as treatment was performed [[32]]. Interestingly, significant differences in microbiome composition were found between
individuals who responded to CBT treatment versus treatment non-responders [[32]]. This apparent conflict in early results, along with relatively small cohort size,
suggest that this is an area that will benefit from efforts to store, share, and re-use
microbiome data, as well as efforts to aggregate data for comparison in an unbiased
and rigorous way. This concept is supported by a fourth study reviewed in the survey:
one study reviewed of women with IBS concluded that there was evidence for further
investigation of the relationship between bile acid levels, the microbiome, and its
mechanism or role in IBS [[45]]. These studies demonstrate a changing microbiome in individuals with IBS, but highlight
the need for evidence to understand the role of the microbiome in IBS and potential
means for treatment.
Type 2 Diabetes. Type 2 diabetes (T2D) in the United States has a similarly sized impact on public
health: according to the CDC, 33-35 million Americans are estimated to have T2D as
of December 2021 [[74]]. The gut microbiome has also emerged as a focus within the research community to
understand T2D and identify treatments and prevention methods [[4], [75]
[76]
[77]]. Although T2D is a highly researched disease, only one study found passed the inclusion
and exclusion criteria. In this study, 405 individuals with T2D found significant
differences in taxa present in the fecal microbiome at the genus level according to
disease severity [[42]]. A cursory search of the query “T2D AND microbiome” alone on PubMed for 2021-2023
revealed 1,051 resulting papers, although inclusion of the terms “diet” or “nutrition”
on the query vastly reduced the search results. One study from management of lifestyle
factors for T2D using digital health applications or mHealth [[55]] indicates that our search terms used may have excluded some relevant papers in
this area.
Amyotrophic Lateral Sclerosis (ALS). The microbiome is also being investigated in diseases not traditionally thought to
be “diet-driven”. For examples, a study of 66 individuals with Amyotrophic Lateral
Sclerosis (ALS) and 73 controls found a significant difference in abundance of certain
taxa in the fecal microbiota of individuals with ALS [[44]]. Outside of this survey, there is some research on diet and development of ALS,
but diet is not currently considered a causal factor [[78], [79]]. It is important then to recognize that although there is evidence of the impact
of diet on the microbiome, that the microbiome may also be a potential tool for prevention,
diagnosis, and treatment of diseases not traditionally considered “diet-driven”. Rather,
the microbiome should be considered an important factor in human health that can be
modified by dietary behaviors.
Pregnancy. Three studies focused on microbiome during pregnancy and during the postpartum period.
A study of n=115 pregnant individuals with and without gestational diabetes found no significant
difference in microbiome composition or alpha diversity, although some significant
changes in bacteria at the genus level were found in the third trimester [[26]]. The authors of this study state that their work adds to existing studies [[76], [80]
[81]
[82]
[83]] on microbiome changes in pregnancy, noting a knowledge gap and need for further
studies of the microbiome during gestation [[26]]. A 2022 study of 90 infant-mother pairs examined the relationship between maternal
weight (overweight or obese) on infant microbiome, also finding no significant associations
between the microbiomes of infants born to individuals who had developed gestational
diabetes versus not [[31]]. Outside of gestational diabetes, a study of n=48 pregnant individuals found evidence for an association between diet and decreased
alpha diversity in the fecal microbiome, speculating that pre-term birth may have
links to the microbiome [[41]]. This implicates diet as a modifiable factor through which maternal and infant
health can potentially be addressed [[41]]. These studies again demonstrate a changing microbiome in pregnant individuals
but highlight the need for evidence to understand the role of the microbiome before,
during, and after pregnancy. The literature reviewed also suggests the importance
of postnatal support for caregivers of infants and children in managing household
tasks. This trend is continued in the Infant Diet themed literature, below.
Infant Diet. Food allergies and intolerance in infancy emerged as a trend in microbiome studies
examined. One study on 30 infants examined microbiome composition between infants
fed a typical cow’s milk-based formula versus a hydrolyzed formula (often used for
infants with dairy intolerances), observing significant differences in microbial composition
after 4 months, as well as observing Ruminococcus gnavus as a taxa on that significantly differentiates between the two groups [[29]]. Another study of 148 infants with a cow’s milk protein allergy found a significant
decrease in symptoms, caregiver burden and healthcare resources when a symbiotic was
prescribed alongside specialized formulas versus no symbiotic [[38]]. Considering the rapid growth and establishment of the infant gut microbiome as
well as its impact on health, it is unsurprising that these studies on infant diet
and microbiome have begun to emerge. A study of 28 preterm infants found a significant
difference between microbiome composition and growth in head circumference, especially
in the phyla Bacteriodota and family Lachnospiraceae [[33]]. This literature also implicitly suggests the importance of postnatal support for
caregivers of infants and children in managing household tasks such as feeding, grocery
purchasing, and food preparation especially in infants with specialized feeding needs.
These needs may be addressed using digital health interventions or informatics approaches.
Microbiome and Diet. Five studies focused on understanding diet and the microbiome, continuing an existing
trend in the literature. A 2022 review described the current knowledge about the role
of the gut microbiome in lipid metabolism and short chain fatty acid modulation [[28]]. The authors acknowledge the impact of diet on the microbiome, including how quickly
the microbiome reacts to changes in dietary composition, timing of meals, fiber intake,
and impact of micronutrients [[28]]). One study performed a week-long at-home immersion experience for 74 participants
focusing on improving behaviors in physical activity and diet [[35]]. The authors report that anti-inflammatory taxa increased in the microbiome of
participants after the intervention [[35]]. Another study examining long term dietary intake effects on the microbiome (n=128 adults) found an association between self-reported carbohydrate intake and gut
microbiome composition [[44]]. A study of 59 individuals aged 40-85 found no significant changes in fecal microbiome
composition’s alpha diversity by age. However, authors did report age-related differences
in microbiome composition from samples taken from salivary and gastrointestinal sites
[[37]]. The results of these four studies highlight a need for larger microbiome studies,
how the microbiome is captured, and the diversity of evidence that is building our
understanding of the gut microbiome. Lastly, a larger cohort study of diet and microbiome
in n=3,308 participants reported taxa that was able to differentiate between individuals
consuming high levels of animal protein versus low levels of animal protein [[34]].
3.2 Usability and Accessibility of Digital Health Applications
This literature survey focused on digital interventions that could be self-managed:
this includes improving, tracking, or monitoring modifiable behaviors for patients
or consumers who are managing their health at home. The subthemes that emerged from
this literature were consistent despite a broad array of research topics and foci:
Applications intended to support or enhance lifestyle factors impacting health need
to be easy to use, easy to learn, fast, accessible, and perceived as useful. Noncompliance
was when users experienced technical issues, or when an application or intervention
was not convenient to use.
There is a wealth of diet-driven applications already online: food trackers, meal
planners, label scanning applications, weight loss programs, and fasting trackers
are all examples. Demand for consumer support in pursuit of health and wellness is
high. Similarly, a focus on usability and feasibility of prototype applications and
interventions emerged in the literature.
Applications Examining Diet or Physical Activity Exclusively. Four studies focusing on exclusively diet or physical activity applications were
reviewed. A web-based application for management of dietary patterns, eNutri, was
evaluated for usability using the System Usability Scale using n=106 participants in Germany [[24]]. Participant feedback demonstrated above average usability but stated concerns
about the amount of time required by the application to complete its purpose (26.7
minutes, mean) [[24]]. Another approach aimed to design a “user-centered” dietary management tool for
type 2 diabetics, surveying 21 individuals over 4 project phases to understand user
needs [[50]]. The study revealed participant’s desire for ease of access to information, ease
of communication, provision of information/content that is easy to understand to accommodate
a busy lifestyle [[50]]. Physical activity-only interventions were also reviewed. A 2022 mHealth study
of a smartphone application designed to encourage physical activity was performed
to compare usability and enjoyment [[46]]. A total of 20 participants gave feedback on the system, and results indicated
that technical issues when using the application negatively affect use [[46]]. A separate study of a Bluetooth-enabled resistance band for enhancing strength
reported positive feedback on usability in terms of ease of use, ease of learning,
and user satisfaction [[56]]. These studies highlight the importance of user-centered design in lifestyle management
applications designed to support behaviors that improve positive health outcomes.
Diet, Sleep, and Physical Activity. Four studies examined applications or interventions designed to support multiple
lifestyle factors versus one single factor (i.e., diet, exercise, sleep), especially
in vulnerable patient populations. Authors of a 2022 study (n=17) on user experience with a web-based weight management application found important
factors to enhance use for self-management of diet and physical activity in kidney
transplant recipients [[47]]. An mHealth application supporting healthy diet, physical activity, and sleep habits
for wheelchair users (n=14) also concluded that successful user engagement relied on ease of use, usefulness,
and ease of learning [[23]]. For this study, users also demonstrated interest in personalization of the application,
ability to access user history, and access to personalized insights based on their
input data and behaviors [[23]]. A prototype application designed to support improved health behaviors in n=50 prediabetic participants shared results supporting a focus on applications that
are easy to use and perceived as useful [[55]]. A much larger study of over 16,000 users of a phone-based app for self-management
of T2D reported that app engagement was associated with improved patient outcomes
(as measured by blood A1C) [[49]].
Applications for Parents and Caregivers. As described previously, food allergies and intolerance in infancy emerged as a trend
in microbiome studies examined. Mirroring this trend, four studies were reviewed that
reflect the use of technology to support parents and caregivers. A 2022 study of n=126 postpartum individuals evaluated subject use of applications meant to track infant
feeding patterns, including feeding times, duration, volume of feed, and more [[48]]. Most of the subjects (n=72) who used the infant feeding application used it for logging or tracking; factors
describing their support for use of a tracking app included ease of use and ability
to use with a co-parent or co-caregiver [[48]]. A 3-phase study to evaluate design needs for a social and emotional well-being
application in Aboriginal and Torres Strait Islander women concluded that app design
for this approach requires extensive end-user consultation and investment in user-centered
design [[53]]. A study of n=45 parents of newborn infants was performed to discern end-user needs for a chatbot
application supporting parental sleep habits and infant feeding in the first 6 months
of life [[54]]. Some of the findings of this study included a desire for short interactions within
the application, willingness to share their data, and noncompliance due to technical
issues, lack of sleep, and physical discomfort [[54]]. A web-based app designed for menu planning was studied for usability by 64 childcare
services employees in Australia; feedback noted by the authors described a need for
improvements in speed and ease of use [[57]]. Feedback also demonstrated majority enthusiasm and usefulness of a menu planning
application for use in their daycare center [[57]].
Virtual Health Assistants. A 2021 review of the literature on virtual health assistants (n=48) examined the user experience of interactive information sharing resources (virtual
health assistants or chatbots) based on their visual design and conversational style,
recommending focus on empathetic interaction, humanistic visual and conversational
designs would fare best [[52]].
3.3 Reproducibility and Rigor of Computational Analysis in the Microbiome
The literature on microbiome sample collection, sample processing, nucleotide extraction,
sequencing, and data freely acknowledges concerns around data quality, reproducibility,
and the need for quality assessment and control. As microbiome data gathering becomes
cheaper and calls for improved biomedical data management standards increase [[84]], the need for these methods will continue to grow. Tools, methods, or calls for
enhanced data management infrastructure, re-use, and code sharing were described.
Removal of Bias in Microbiome Analysis. Many microbiome datasets have small sample sizes, and there is interest in means
to compare, combine, or otherwise aggregate results to see which findings can be generalized.
Methods, applications, and recommendations for this type of broad scale comparison
and quality assessment in microbiome data were proposed in the literature to correct
for environmental batch effects [[58]], for population-level data stratification [[36]], filtering of rare taxa for reproducibility and generalizability [[63]], among others. Reproducibility continues to be present as a topic of interest,
including a method (RESCRIPt) for enhancing reproducibility of reference databases
commonly used for taxonomic identification in microbiome analysis [[60]].
A review performed in response to challenges in defining a microbial association network
in an environment that is not biased by experimental or computational artifacts, calling
for focus on benchmarking and validation [[62]]. Development of minimum information standards, called the STORMS checklist, for
microbiome research that recognizes the interdisciplinary nature of microbiome research,
and the data management processes that must be in place to enhance reproducibility
and replicability [[59]]. There is a need and enthusiasm for training materials on microbiome composition
analysis as demonstrated by sessions provided for the microbiome analysis software
QIIME2, with requests for additional trainings on reproducibility and workflow documentation
[[61]].
The Future of Artificial Intelligence in Microbiome Research. A review on machine learning in the microbiome space proposed recommendations for
reliable application of artificial intelligence for precision medicine, including
creation of standards, increase in quantity and quality of microbiome data, application
of appropriate data management solutions such as the Findable, Accessible, Interoperable,
and Reusable (FAIR) data principles, and support for interdisciplinary team science
[[66]]. A similar review examining machine learning challenges in human microbiome data
echoes a need for larger studies of a certain quality, experimental and computational
bias, and need for interpretability of machine learning model outputs [[67]].
3.4 Precision Medicine and Precision Nutrition
Precision medicine and precision nutrition were emerging topics that encompassed multiple
disciplines within the computational health and biology space. There is massive interest
in the role of the microbiome in precision medicine [[85]
[86]
[87]], both generally and for specific applications such as the treatment of cancer [[88]] and to enhance pharmacologic intervention [[89]]. This interest extends to the relationship between nutrition and the microbiome.
In May 2020, the National Institutes of Health described the role of nutrition informatics
in its 2020-2030 Strategic Plan for NIH Nutrition Research, which details four questions
as a part of its strategic approach: “What do we eat and how does it affect us?, “What and when should we eat?”, “How does what we eat promote health across our lifespan?”, and “How can we improve the use of food as medicine?” [[90]]. Answering these high-level questions requires interdisciplinary, team-science
based approaches that span the translational science spectrum, from bench to bedside.
As methods for capturing dietary behavioral data, food composition, provenance, and
preparation data improve, and our understanding of the impact of dietary patterns
on the microbiome improves, it is possible to imagine a future where the interface
of bioinformatics and health informatics is more clearly realized.
Future Trends. Trends discussed through research studies and reviews examined clearly outline diet-driven
diseases as a target for precision medicine and nutrition. Obesity is one a high-impact
target for the development and application of precision nutrition approaches. A 2021
review predicts that future applications of microbiome research to benefit precision
nutrition will include manipulation of the gut microbiome through diet, pre- and probiotic
supplementation to alter microbiome composition, as well as fecal microbiota transplantation
for treatment of disease [[25]]. A perspective article in PNAS notes the relationship between the microbiome and
health inequities [[65]]. Therefore, it can be recognized as a tool for researchers to examine as a modifiable
factor to improve human health in those populations experiencing health inequity [[65]]. One study, upon finding differences in fecal microbiome taxa between obese and
non-obese African American children (n=30) aged 6-10 years old, speculated on a need for personalized approaches that are
inclusive of ethnicity and other factors such as socioeconomic status [[40]]. A 2022 review on racial disparities and cardiovascular health notes the role of
dietary behaviors and environmental factors on the microbiome, stating that they will
likely factor into “precision medicine” to improve outcomes [[51]]. Other angles include the examination of dietary trends over longer periods of
time. For example, another review described the “nutribiography”, a collection of
behavior over the lifetime is proposed as a potential future means for examining the
impact of long-term diet on inflammation and aging [[64]]. Research in this space appears to be trending towards the understanding of food
environment and socioeconomic factors in supporting consumer purchasing and dietary
choices leading to positive health outcomes. This work recognizes the need for improved
nutrition literacy and improved food access, as well as accessible and clear product
labeling.
4 Discussion
Technology is allowing for the scientific community to bridge the gap between health
informatics and bioinformatics for the understanding of nutrition and the microbiome.
This work identified major themes in (1) Microbiome and Diet-driven Disease, (2) Usability
and Accessibility of Consumer Health Tools, (3) Reproducibility and Rigor of Computational
Analysis in the Microbiome, and (4) Precision Medicine and Precision Nutrition. While
each of the individual themes on usability and accessibility, microbiome and diet,
and reproducibility has a clearly defined limit to its scope, these limits have potential
for overlap through the application of technology and informatics. The possibility
of digital dietary applications for supporting modification of the gut microbiome
has great potential for improving health outcomes, for example. However, researchers
pursuing these pathways must also be aware of the need for user-centered design in
their applications, the reality of food access challenges, and the need for rigorous
research supporting recommendations based on existing microbiome data.
Approximately 60% of the United States population experiences diet-related chronic
disease such as overweight/obesity, heart disease, stroke, or T2D [[91], [92]]. Approximately 56 million adults aged 65 and older currently live in the United
States, and up to 60% of those individuals are estimated to experience malnutrition
[[93],[94]]. Another 10% of the United States population experiences physician-diagnosed food
allergies and/or intolerances [[95], [96]]. There is great potential to enhance disease prevention via technology and precision
nutrition, but research in this area must address the factors of health equity that
play into making dietary choices that fuel positive outcomes. This includes developing
technology to support those choices that is easy to use, easy to learn, fast, personalized,
and addresses gaps in health literacy found in vulnerable populations [[97]
[98]
[99]]. This also includes acknowledgement of research demonstrating that purchase and
preparation of nutritious food in the home requires time, effort, and support [[20], [100], [101]].
From a bioinformatics perspective, there is great potential for continued microbiome
research and its relationship to dietary patterns to enhance human health. The literature
reviewed on microbiome research in this survey demonstrates a need for methods to
compare and aggregate data, a need for reporting standards, workforce training, and
needs for understanding challenges in both experimental and computational reproducibility
and generalizability in microbiome analysis. This requires engagement of the informatics
community and resources to build data sharing and re-use infrastructure, as well as
communication of biases and challenges in data analysis. This charge is supported
by existing work in the literature [[102]
[103]
[104]
[105]].