Data-Driven Precision Nutrition Improves Clinical Outcomes and Risk Scores for IBS,Depression,Anxiety,and T2D

Abstract

Many studies have shown that foods and nutritional ingredients play an important role in healthy human homeostasis, either directly or via the microbiome. We have developed an objective, integrated, and automated approach to personalized food and supplement recommendations that is powered by artificial intelligence and individualized molecular data from the gut microbiome, the human host, and their interactions. The process starts with a clinically validated transcriptomic analysis of a person’s stool (and some cases also blood) sample. These molecular data are converted into personalized nutritional recommendations (foods and supplements) using algorithms derived from clinical research studies and domain knowledge. We describe an application of our precision nutrition technology platform to human populations with irritable bowel syndrome (IBS), depression, anxiety, and type 2 diabetes (T2D). In these pilot interventional studies, our precision nutrition program achieved significant improvements in clinical outcomes of IBS (39% for severe IBS), depression (31% for severe depression), anxiety (31% for severe anxiety), and the risk score for T2D (>30% reduction relative to the control arm). These data support the integration of data-driven precision nutrition into the standard of care.

Keywords

precision nutrition irritable bowel syndrome depression anxiety type 2 diabetes

“The key to understanding the effects of the gut microbiome on human physiology and health is to quantify the microbial functions using metatranscriptomic, metaproteomic, or metabolomic approaches.”

Introduction

There is strong epidemiological and molecular evidence that the human diet contributes significantly to the onset and progression of many chronic diseases and cancers.^1–6 Dietary advice, however, has been controversial, conflicting and polarized. Scientific publications often contradict one another, with one demonstrating health benefits while another suggests potential harm for the same foods or diets.^7–10 For example, one epidemiological study reports that daily consumption of fruit juice raises cancer rates significantly.¹¹ Other studies report that the consumption of daily fruit or fruit juice can actually prevent cancer.^12–14 Omega 3s have been described as beneficial for mental health, but there are studies that refute such claims.^15,16 Dairy consumption has also generated contradictory claims, with some evidence supporting an association with a higher incidence of some cancers and chronic diseases, some studies showing no such correlations, while other studies demonstrate significant benefits, and USDA recommends dairy to Americans.^3,17–20 Likewise, recent publications provide conflicting data on the effects of saturated fats on cardiovascular and metabolic diseases.^17,18,21,22 Red meats have been touted as healthy sources of critical nutrients, such as vitamin B12 and dietary protein, and even shown to provide health benefits; yet there is strong epidemiological and mechanistic evidence that they may be responsible for increased rates of inflammatory diseases and cancers.^1,23-25 To complicate the landscape further, current food and supplement guidelines are influenced by the food industry, which promotes the increased consumption of sugars, dairy, and meat.^26,27

The large body of conflicting food and supplement information has manifested in the creation of hundreds of diets, each claiming health benefits. When it comes to scientific data, the vast majority of food and supplement research does not account for the specific and unique contributions of an individual’s gut microbiome, despite a multitude of evidence showing that the impact is significant.^28–32 In addition, most dietary guidelines generally focus on food groups (vegetables) rather than single foods (broccoli), assuming that the same food will have the same effect on different people. The USDA-provided nutritional guidelines are the same for everyone and based on population averages (https://www.dietaryguidelines.gov/). These guidelines do not consider personal preferences, food intolerances or food allergies either. Yet, the emerging science of the microbiome informs us that each microbiome is like a fingerprint, unique to each one of us.³³ Now there is strong scientific evidence that genetics plays a minor role, and the gut microbiome plays a major role in the effects of nutrition on human physiology.^34,35 This was demonstrated in a large twins study of metabolic disease parameters.³¹ This study observed large inter-individual variability in postprandial blood triglyceride, glucose, and insulin responses to identical meals. It is clear that the gut microbiome not only influences how food is digested, but also converts many of the molecular ingredients found in foods into beneficial or harmful secondary metabolites that have profound effects on human physiological functions, such as neurotransmitter production, immune system activation and deactivation (inflammation), immune tolerance, and carbohydrate metabolism.^36–40

Hence, the metabolic functions of each person’s unique microbiome need to be identified and quantified, in order to “prescribe” specific foods that contain molecular ingredients that will be converted to physiologically beneficial secondary metabolites by that person’s individual gut microbiome. Microbial functions also need to be quantified so each person can avoid foods that contain molecular ingredients that will be converted by their gut microbiome into pro-inflammatory, and therefore harmful secondary metabolites that are associated with disease.

In addition to the approaches that utilize known microbial metabolites and their micronutrient precursors, machine-learned models from large clinical studies need to be applied to each person’s gut microbiome analysis to guide the best choice of foods and supplements. Such methods have already been applied by several groups, including ours, using a variety of features to develop personalized nutrition algorithms.⁴¹ The features used for machine learning range from biometric and demographic data to molecular data from genetic and microbiome analyses. While other groups have used microbiome data as input features, we believe our group is the only one that uses microbiome metatranscriptomic features for machine learning. This should provide a significant advantage over other models, since the level of gene expression, not a mere presence of genes or genomes, determines the observed phenotype.

In this paper, we describe a data-driven approach to improve clinical outcomes using precision nutrition that are computed by artificial intelligence algorithms using each person’s molecular data and the self-reported phenotype. This approach is completely algorithmic and data-driven, and does not include any opinions, anecdotes, experiences, or pre-determined diets (e.g., ketogenic, plant-based, or Mediterranean). The molecular data are obtained from stool or a combination of stool and blood samples using highly accurate and reproducible, clinically validated and licensed transcriptomic tests.^42,43 These data are used to quantify the activity of microbial metabolic pathways, specific microbial taxa (at the strain level), and human gene expression levels. This information is then converted to personalized foods and supplement recommendations. An overview of this approach is shown in Figure 1 and described in detail in the sections below.

Figure 1.

AI-generated precision food and supplement recommendations. Stool and blood samples are collected from participants. The RNA is extracted and sequenced using clinically licensed laboratory and bioinformatics methods. Microbial and human gene expression, including KOs (KEGG orthologs) are quantified from the RNA sequencing data of each participant. Functional scores are subsequently computed as a weighted function of relevant KOs. Finally, personalized nutrition recommendations are computed using all functional scores and phenotypes. All these steps were conducted only once and before the collection of the questionnaire-based endpoints and initiation of nutritional intervention.

Materials and Methods

Human Subjects, Ethical Considerations, and Study Design

The clinical studies described here were either approved by a federally-accredited Institutional Review Board (IRB), or were conducted using the data from consumers that consented to their data being used for research purposes. All samples and metadata were obtained from people at least 18 years old and residing in the USA at the time of participation. All study participants provided informed consent to participate in the studies. The study design was non-conventional, as there was no control arm in three of the studies, due to the limitations of the commercial product. The subjects were blinded to the fact that they were participating in an interventional study with clinical endpoints. All study participants were recruited into a “wellness study” that asked them to complete a wellness survey. These survey questions were clinically validated surveys for depression (PHQ9), anxiety (GAD7), and irritable bowel syndrome (IBS) (Rome IV criteria).^42-47 Type 2 diabetes (T2D) risk score (0 to 100) was used as the clinical endpoint for T2D.⁴⁸ The T2D trial included two arms, based on the self-reported adherence of each participant, low or high. To evaluate the effect of adhering to the precision recommendations, we compared the difference in T2D risk scores between the “low adherence” cohort (control arm) and the “high adherence” cohort (interventional arm), while controlling for other variables. The two cohorts were matched by (a) initial risk score of +/−10 points (i.e., each participant in one cohort had a participant in the other cohort within 20 points of their risk score at the first time point) and (b) period of adherence of +/− 60 days (i.e., within a total of 120 days), yielding a cohort size of N = 1456 for each arm, evaluated over a mean time period of ∼261 +/− ∼132 days and ∼256 +/− ∼133 days for the two arms respectively.

The primary endpoints for the four conditions were:

• IBS: IBS Symptom Severity Score (IBS-SSS), as part of the Rome IV criteria (the Rome Foundation), a clinically validated questionnaire on a scale of 0 through 500, with 300+ indicating severe IBS, 175 to 299 indicating moderate IBS, and 75 to 174 indicating mild IBS.^44,45

• Depression: PHQ9 score, a clinically validated questionnaire of 9 questions that yields a score of 0 through 27, with 15+ indicating moderately severe or severe depression, 10 to 14 indicating moderate depression, and 5 to 9 indicating mild depression.^46,47

• Anxiety: GAD7 score, a clinically validated questionnaire of 7 questions that yields a score of 0 through 21, with 15+ indicating severe anxiety, 10 to 14 indicating moderate anxiety, and 5 to 9 indicating mild anxiety.⁴²

• T2D: we used a recently developed T2D risk score, which was built using data from the stool metatranscriptomic analyses of over 50,000 subjects, and validated against a clinical measurement of HbA1c within an independent cohort of over 2200 subjects.⁴⁸

Each interventional trial had a single arm for participants with depression, anxiety and IBS, and two arms for participants with T2D (high and low adherence). At time point one (T1), participants were asked to complete the wellness questionnaire just prior to receiving their precision food and supplement recommendations. At time point two (T2), each participant was asked to fill out the wellness questionnaire again. There were no additional communications with the participants between the two time points. The clinical studies described here were registered at ClinicalTrials.gov with identifiers NCT04905524 and NCT04905485.

Metatranscriptomic Analyses of Stool and Transcriptomic Analyses of Capillary Blood Samples

The molecular analyses for the studies reported here focus on sequencing messenger RNAs (mRNAs) isolated from human stool and blood samples. Stool and blood samples were collected and analyzed as previously reported.^49,50

Metabolic Pathways and Functional Scores

We have designed functional scores to quantify certain physiological phenomena; for example, leaky gut, inflammation, gas production, protein fermentation, cellular health, and mitochondrial health.⁵¹ These scores are relevant to human physiology and healthy homeostasis. Functional scores are weighted functions (Score = C₁F₁ + C₂F₂ + … + C_nF_n, where F is the feature and C is its weight) of specific components from the transcriptomic data. The features or components that make up the functional scores can be taxa, microbial pathways, human pathways, or a combination of other functional scores. Functional scores range from 0 to 100, with 100 representing the highest possible activity. For example, a microbiome-derived functional score such as Butyrate Production Pathways is calculated as a weighted function consisting of the expression levels of many known butyrate-associated KOs (Figure 2).⁵² The expression level of each KO from the Butyrate Production Pathways score is quantified using the metatranscriptomic stool test and the score is then computed using the weighted formula.⁵³ The weights attributed to each KO within a pathway are determined by a combination of domain knowledge (e.g., Figure 2) and statistical analyses obtained from a collection of stool samples.^54,55 By quantifying each enzymatic member (KO) of the butyrate pathways using microbial gene expression data, estimated pathway activities can be calculated.⁵⁶

Figure 2.

Microbial butanoate (butyrate) production pathways based on KEGG annotations.

Precision Nutrition Program

Viome Precision Nutrition Program (VPNP) includes precision food and personalized supplement recommendations computed by the Viome AI Recommendation Engine. VPNP is designed to maintain the healthy homeostasis at the functional level by increasing beneficial (health-associated) and reducing harmful (disease-associated) biological activities using molecular ingredients from foods and supplements (Figure 1). This approach is built on the concept that on a molecular level, a particular food or supplement is beneficial for one person, but harmful to a different person. Table 1 shows examples of this concept. The Viome AI Recommendation Engine uses a sophisticated set of algorithms to determine the final food and supplement recommendations. The algorithms are developed from domain knowledge (publications on microbial and human physiology, food science, clinical trials, etc.) and extensive clinical studies from which machine-learned models of food and supplement modulation of the microbiome and human physiology were developed.²⁸ These algorithms are applied to the functional scores for each person whose stool, or stool and blood samples are analyzed.

Table 1.

Examples of Specific Foods and Supplements, and Reasons for Recommendation to Consume or Avoid.

Food/Supplement	Reasons to Consume	Reasons to Avoid
Artichoke	- Low butyrate production pathways functional score, and	- High gas production functional score, or
Artichoke	- Presence of specific microbial strains that convert inulin into butyrate	- Small intestinal bacterial overgrowth (SIBO) symptoms
Broccoli	- High inflammatory activity functional score, or	- High sulfide gas production functional score, and
Broccoli	- Suspected liver and detox support needed	- High trimethylamine (TMA) production functional score
Trout	High inflammatory activity functional score	High uric acid production pathways functional score
Nicotinamide Riboside (NR)	- Low mitochondrial health functional score, or	- High cellular senescence functional score, and
Nicotinamide Riboside (NR)	- Low mitochondrial biogenesis pathways functional score	-High inflammatory activity functional score
Curcumin	- High inflammatory activity functional score, or	High bile acid production pathway functional score
	- High cellular stress, or
	- High immune system activation

The Viome AI Recommendation Engine considers compounds (molecular ingredients) in foods and supplements that can support the molecular functions of health-associated microbial and human genes. These compounds include specific polysaccharides, polyphenols, vitamins, minerals, amino acids, fatty acids, and many phytochemicals. For example, an almond is a source of many phytonutrients and compounds such as kaempferol (flavonoid), naringenin (flavonoid), ferulic acid (phenolic acid), oxalic acid, phytic acid, quercetin, procyanidin B2 and B3, magnesium, phytosterols such as retinol, a-tocopherol, vitamin K, vitamin D, and beta-sitosterol, fatty acids such as oleic acid, linoleic acid, and palmitic acid, and specific amino acids.⁵⁷ The decision to recommend a specific compound and its amount depends on the values of multiple functional scores. After considering all inputs, the recommendation engine classifies foods into one of four categories, based on the molecular composition of each food. The food categories are superfoods, enjoy foods, minimize foods, and avoid foods, which are consumerized names that correspond to the recommended servings per day for each food.

Personalized supplement recommendations follow the same logic, considering all inputs to identify compounds that are beneficial or harmful to an individual’s functional scores and phenotype. Supplements include minerals, vitamins, botanicals or herbs, food extracts, enzymes, phospholipids, amino acids, prebiotics, and probiotics. When considering individual functional scores, supplement ingredients commonly believed to be beneficial may not be recommended. For example, turmeric is a commonly consumed supplement for its anti-inflammatory properties, but has also been shown to increase bile flow.^58-60 While this was an animal study, it can help to inform us that for individuals with a high Bile Acid Metabolism Pathway functional score, turmeric supplementation may be more harmful than beneficial and must be assessed more carefully. A high Bile Acid Metabolism Pathway score suggests that the microbial activity of transforming bile salts into bile acids is high. While such biotransformation is part of a balanced gut microbiota and bile acid homeostasis, excessive intestinal bile acids may promote a pro-inflammatory environment and play a role in the development of gastrointestinal diseases.^61,62

The process of categorizing foods and supplements (determining the servings or dose) includes prioritizing scores that need improvement and considering conflicts within the recommendations. An example is shown in Table 2: a low Energy Production Pathway functional score will yield recommendations based on compounds that contribute to the score activity, one of which is alpha-lipoic acid (ALA). Spinach and broccoli are recommended due to the ALA content that is a critical cofactor for mitochondrial energy production enzymes such as pyruvate dehydrogenase (PDH), alpha-ketoglutarate dehydrogenase (alpha-KGDH), and branched-chain ketoacid dehydrogenase (BCKDC).⁶³ However, when considering additional score results, broccoli and spinach are placed on the avoid food list due to broccoli’s glucosinolate content and spinach’s oxalate content. Instead, tomatoes and peas are recommended as sources of ALA to support the Energy Production Pathway functional score.

Table 2.

Food Recommendations Case Example.

Example Score Results:					Self-Reported Information on Wellness Questionnaire:
High Sulfide Gas Production Pathways Functional Score					Histamine Intolerance
Low Butyrate Production Pathways Functional Score					Pistachio Allergy
Low Oxalate Metabolism Pathways Functional Score
Low Energy Production Pathways Functional Score

Food	Compounds	Food Categories (Initial)	Final Recommendation	Replacement Food/Supplement
Spinach	- Alpha-lipoic acid	- Superfood, based on low energy production pathways functional score	Avoid	- Tomato and
Spinach	- Oxalates	- Avoid, due to low oxalate metabolism pathways functional score	Avoid	- alpha-lipoic acid supplement
Broccoli	- Alpha-lipoic acid	- Superfood, based on low energy production pathways functional score	Avoid	- Peas and
Broccoli	- Glucosinolates	- Avoid, due to high sulfide gas production pathways functional score	Avoid	- alpha-lipoic acid supplement
Sauerkraut	Probiotics	- Superfood, based on low butyrate production pathways functional score	Minimize	- Probiotic supplement that includes Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus rhamnosus
Sauerkraut	Probiotics	- Minimize, due to self-reported histamine intolerance	Minimize
Pistachios	CoQ10	Superfood for energy production pathways low	Avoid	CoQ10 supplement
Pistachios	CoQ10	Avoid due to self-reported pistachio allergy	Avoid	CoQ10 supplement
Grapes	Resveratrol	Superfood for energy production pathways low	Superfood

There are circumstances where a beneficial compound cannot be obtained from food due to an allergy or other health-related issue, or due to the lack of sufficient amounts in food. Personalized supplements help support those gaps in nutrition. In Table 2, pistachios are recommended due to their CoQ10 content. However, if an individual has an allergy to pistachios or exhibits small intestinal bacterial overgrowth (SIBO) symptoms, pistachios are placed on the avoid or minimize food list. In this situation, CoQ10 can be provided through a supplement as the recommendation engine associates it as beneficial for the same score and/or phenotypic conditions.

Results

We conducted four interventional studies that tested the efficacy of VPNP to improve clinical or molecular scores for IBS, depression, anxiety, and T2D. Three of the studies included only an interventional arm (IBS-SSS, n = 105; PHQ9, n = 410; GAD7, n = 490); the T2D study also included a control arm (1456 low adherence vs 1456 high adherence).

Figure 3A-C shows the changes in the clinical study endpoints following VPNP. We observe an improvement in the clinical scores for all three conditions (IBS, depression, and anxiety) and all disease activity levels. We note that the P-value for mild depression score improvement is .2038, which is above the conventional standard of .05. However, the improvement in all other clinical outcomes, conditions and severity levels are highly statistically significant.

Figure 3.

Efficacy of the food and supplement interventions on participants with IBS, depression, or anxiety. (A). Results showing the effect of precision food and supplement recommendations on subjects with IBS, measured using the IBS-SSS score. Bars on the right are the mean +/− standard deviation of IBS-SSS score; blue bars represent the baseline (at the study start) and orange bars represent the followup (after the nutritional intervention). (B). Results showing the effect of precision food and supplement recommendations on subjects with depression, measured using the PHQ9 score. Bars on the right are the mean +/− standard deviation of PHQ9 score; blue bars represent the baseline (at the study start) and orange bars represent the followup (after the intervention). (C). Results showing the effect of precision food and supplement recommendations on subjects with anxiety, measured using the GAD7 score. Bars on the right are the mean +/− standard deviation of GAD7 score; blue bars represent the baseline (at the study start) and orange bars represent the followup (after the intervention).

Figure 4 shows the difference in the change of the T2D risk score (y axis) between the low-adherent participants (blue box plot) and high-adherent participants (green box plot). We observe a statistically significant risk score improvement for people with high adherence (P = 1.99e-05), with a mean risk score reduction of 7 points [(-30.25) - (−23.21)], which translates to >30% improvement. This shows that high adherence to the precision nutrition recommendations results in a lower risk of T2D.

Figure 4.

Efficacy of the food and supplement interventions on participants with an elevated T2D risk score. Participants who adhered to precision recommendations (green box) reduced their T2D risk score more than participants who did not (blue box) (p < 0.001).

Dietary recommendations were delivered via a smartphone application and website. These can be viewed in different ways, but are summarized in a comprehensive, alphabetical list in Figure S1. Each food is labeled as either superfood, enjoy, minimize, or avoid, which represents the quantity of that food that should be consumed on a weekly basis. Program participants are to completely avoid the “avoid” foods, consume some “minimize” foods, consume lots of “enjoy” foods, and consume the most weight or volume of the “superfoods.”

Dietary and supplement recommendations are highly personalized, since they are computed from the molecular data obtained from the clinical samples. Figure S2 shows the number of common supplement ingredients (left), avoid foods (middle), and superfoods (right) in 100,000 random pairs of individuals in the Viome population. More than 70% of those pairs have zero common supplement ingredients, 2 or fewer common avoid foods; and 4 or fewer superfoods. The number of pairs of individuals with higher overlaps drops to zero very quickly.

Discussion

We describe a novel technology platform that overcomes some of the major shortcomings of food and supplement science, and improves clinical and molecular outcomes in several highly prevalent chronic diseases, IBS, depression, anxiety, and type 2 diabetes.^64-66 Traditional food and supplement research has been conducted using various methods. Epidemiological studies have been used to derive food and supplement rules using large populations. These have yielded interesting results that have guided certain national-scale dietary recommendations, such as the current USDA guidelines in the USA (Dietary Guidelines for Americans, 2020-2025). Many clinical and pre-clinical studies have also tested the effects of certain foods and diets on various health outcomes. Additionally, many different supplements, including prebiotics and probiotics, and their combinations have been tested for their ability to improve certain symptoms.^67-72

Over the last decade, it has become clear that the gut microbiome plays a crucial role in modulating human physiology by regulating its immune system, metabolic functions, hormones, and neurotransmitters.⁷³ It is important to note that the microbiome’s influence on human physiology is exerted via its functions, and not merely the taxonomy.^40,74 For example, the fact that a person’s gut microbiome contains Faecalibacterium prausnitzii, which is a well-known butyrate producer, does not mean that it is producing butyrate. However, when it encounters the correct substrate, it will produce this important metabolite.^75,76 Escherichia coli is a bacterial species that comprises very beneficial strains that produce vitamins K and B12, and help human hosts efficiently absorb iron; it also represents highly pathogenic, life-threatening microorganisms, such as the enterohemorrhagic strains.^77-79 Therefore, identifying the species of Escherichia coli in a gut microbiome is not very informative, given the vastly different functions that members of this species can perform.

The key to understanding the effects of the gut microbiome on human physiology and health is to quantify the microbial functions using metatranscriptomic, metaproteomic, or metabolomic approaches. We have chosen metatranscriptomics as the best single approach, as it enables quantitative measurements of microbial gene expression that can identify both protein-based and metabolite-based effects on the human physiology, and still provide the highest resolution of taxonomic classification (can distinguish strains) that can guide some aspects of the food and supplement recommendations. Another essential aspect to understanding the relationship between the human physiology, microbiome and diet is to integrate the human molecular data into the systems biology view of the human body. We use a whole blood transcriptome test that can quantify the expression levels of >10,000 human genes from capillary (finger prick) blood.

We also want to emphasize that foods should be viewed as containers of molecular ingredients, instead of objects that we can visually recognize as onions, peppers, etc. The reason for this is that each food contains many different molecular ingredients, as exemplified above, that can exert a multitude of health-related effects on the host, either directly or via the microbiome metabolism.⁸⁰

Study limitations: Three of the four studies (for IBS, depression, and anxiety) were single arm interventional studies without control arms. We also did not capture the level of adherence to the food and supplement recommendations for these three studies, which could significantly affect the observed results; improved adherence may have provided improved efficacy. We did capture this information for the T2D study, and showed that improved adherence improved the molecular scores (Figure 4). It is possible that the observed reductions in the clinical scores would have been higher if the participants with low adherence were excluded from the study. For the T2D study, we matched the study arms for age, sex, and BMI, but did not include other possible confounders. The diet and supplement recommendations were delivered digitally, and no effort was made to either monitor or improve compliance. Because these are preliminary studies without robust control arms, the observed improvements could be explained by the regression to the mean. Repeated measurements over a longer period of time will be conducted in order to determine both the stability of the observed reductions in the symptoms, and also onset of any new symptoms. All of these limitations will be addressed in future randomized controlled trials.

Supplemental Material

Supplemental Material - Data-Driven Precision Nutrition Improves Clinical Outcomes and Risk Scores for IBS, Depression, Anxiety, and T2D

Supplemental Material for Data-Driven Precision Nutrition Improves Clinical Outcomes and Risk Scores for IBS, Depression, Anxiety, and T2D by Janelle Connell, Ryan Toma, Cleo Han-Chen Ho, Nan Shen, Pedro Moura, Tiep Le, Eric Patridge, Grant Antoine, Hilary Keiser, Cristina Julian, Uma Naidoo, Damon Tanton, Guruduth Banavar, and Momchilo Vuyisich in American Journal of Lifestyle Medicine

Supplemental Material

Supplemental Material - Data-Driven Precision Nutrition Improves Clinical Outcomes and Risk Scores for IBS, Depression, Anxiety, and T2D

Footnotes

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Most authors are employees of Viome Life Sciences, which funded the studies.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Eric Patridge

Momchilo Vuyisich

Supplemental Material

Supplemental material for this article is available online.

References

Samraj

Pearce

OMT

Läubli

, et al. A red meat-derived glycan promotes inflammation and cancer progression. Proc Natl Acad Sci USA. 2015;112(2):542-547. doi:10.1073/pnas.1417508112

Sinha

Cross

Graubard

Leitzmann

Schatzkin

. Meat intake and mortality: a prospective study of over half a million people. Arch Intern Med. 2009;169(6):562-571. doi:10.1001/archinternmed.2009.6

Fraser

Jaceldo-Siegl

Orlich

Mashchak

Sirirat

Knutsen

. Dairy, soy, and risk of breast cancer: those confounded milks. Int J Epidemiol. 2020;49(5):1526-1537. doi:10.1093/ije/dyaa007

Ruesten

Avon

Feller

Bergmann

Boeing

. Diet and risk of chronic diseases: results from the first 8 years of follow-up in the EPIC-Potsdam study. Eur J Clin Nutr. 2013;67(4):412-419. doi:10.1038/ejcn.2013.7

Roberts

Barnard

. Effects of exercise and diet on chronic disease. J Appl Physiol. 2005;98(1):3-30. doi:10.1152/japplphysiol.00852.2004

Schulze

Martínez-González

Fung

Lichtenstein

Forouhi

. Food based dietary patterns and chronic disease prevention. Bmj. 2018;361:k2396. doi:10.1136/bmj.k2396

Seidelmann

Claggett

Cheng

, et al. Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. Lancet Public Health. 2018;3(9):e419-e428. doi:10.1016/s2468-2667(18)30135-x

Zhang

Zhuang

, et al. Cooking oil/fat consumption and deaths from cardiometabolic diseases and other causes: prospective analysis of 521,120 individuals. BMC Med. 2021;19(1):92. doi:10.1186/s12916-021-01961-2

Grosso

Godos

Galvano

Giovannucci

. Coffee, caffeine, and health outcomes: an umbrella review. Annu Rev Nutr. 2017;37(1):131-156. doi:10.1146/annurev-nutr-071816-064941

10.

Fleming

Kris-Etherton

Petersen

Baer

. Effect of varying quantities of lean beef as part of a Mediterranean-style dietary pattern on lipids and lipoproteins: a randomized crossover controlled feeding trial. Am J Clin Nutrition. Published online 2021. doi:10.1093/ajcn/nqaa375

11.

Chazelas

Srour

Desmetz

, et al. Sugary drink consumption and risk of cancer: results from NutriNet-Santé prospective cohort. Bmj. 2019;366:l2408. doi:10.1136/bmj.l2408

12.

Khan

Chiavaroli

Zurbau

Sievenpiper

. A lack of consideration of a dose–response relationship can lead to erroneous conclusions regarding 100% fruit juice and the risk of cardiometabolic disease. Eur J Clin Nutr. 2019;73(12):1556-1560. doi:10.1038/s41430-019-0514-x

13.

Clemens

Drewnowski

Ferruzzi

Toner

Welland

. Squeezing fact from fiction about 100% fruit juice. Adv Nutrition Int Rev J. 2015;6(2):236S-243S. doi:10.3945/an.114.007328

14.

Key

. Fruit and vegetables and cancer risk. Brit J Cancer. 2011;104(1):6-11. doi:10.1038/sj.bjc.6606032

15.

Okereke

Vyas

Mischoulon

, et al. Effect of long-term supplementation with marine omega-3 fatty acids vs placebo on risk of depression or clinically relevant depressive symptoms and on change in mood scores. JAMA. 2021;326(23):2385-2394. doi:10.1001/jama.2021.21187

16.

DiNicolantonio

O’Keefe

. The importance of marine omega-3s for brain development and the prevention and treatment of behavior, mood, and other brain disorders. Nutrients. 2020;12(8):2333. doi:10.3390/nu12082333

17.

Warensjo

Nolan

Tapsell

. Chapter 1 dairy food consumption and obesity-related chronic disease. Adv Food Nutrition Res. 2010;59:1-41. doi:10.1016/s1043-4526(10)59001-6

18.

Song

Liu

. Dairy in human health and disease across the lifespan. Sect Iv Dairy Funct Food Heal Dis. Published online. 2017:423-428. doi:10.1016/b978-0-12-809868-4.00033-9

19.

Dehghan

Mente

Rangarajan

, et al. Association of dairy intake with cardiovascular disease and mortality in 21 countries from five continents (PURE): a prospective cohort study. Lancet. 2018;392(10161):2288-2297. doi:10.1016/s0140-6736(18)31812-9

20.

Melnik

. Evidence for acne-promoting effects of milk and other insulinotropic dairy products. Nestlé Nutrition Inst Work Ser. 2011;67:131-145. doi:10.1159/000325580

21.

Ströher

Oliveira

MFde

Martinez-Oliveira

, et al. Virgin coconut oil associated with high-fat diet induces metabolic dysfunctions, adipose inflammation, and hepatic lipid accumulation. J Med Food. 2020;23(7):689-698. doi:10.1089/jmf.2019.0172

22.

Tholstrup

Ehnholm

Jauhiainen

, et al. Effects of medium-chain fatty acids and oleic acid on blood lipids, lipoproteins, glucose, insulin, and lipid transfer protein activities. Am J Clin Nutrition. 2004;79(4):564-569. doi:10.1093/ajcn/79.4.564

23.

Astrup

Magkos

Bier

, et al. Saturated fats and health: a reassessment and proposal for food-based recommendations JACC state-of-the-art review. J Am Coll Cardiol. 2020;76(7):844-857. doi:10.1016/j.jacc.2020.05.077

24.

O’Connor

Paddon-Jones

Wright

Campbell

. A Mediterranean-style eating pattern with lean, unprocessed red meat has cardiometabolic benefits for adults who are overweight or obese in a randomized, crossover, controlled feeding trial. Am J Clin Nutrition. 2018;108(1):33-40. doi:10.1093/ajcn/nqy075

25.

Wang

Bergeron

Levison

, et al. Impact of chronic dietary red meat, white meat, or non-meat protein on trimethylamine N-oxide metabolism and renal excretion in healthy men and women. Eur Heart J. 2018;40(7):583-594. doi:10.1093/eurheartj/ehy799

26.

Bes-Rastrollo

Schulze

Ruiz-Canela

Martinez-Gonzalez

. Financial conflicts of interest and reporting bias regarding the association between sugar-sweetened beverages and weight gain: a systematic review of systematic reviews. PLoS Med. 2013;10(12):e1001578. doi:10.1371/journal.pmed.1001578

27.

Kearns

Glantz

Schmidt

. Sugar industry influence on the scientific agenda of the national institute of dental research’s 1971 national caries program: a historical analysis of internal documents. PLoS Med. 2015;12(3):e1001798. doi:10.1371/journal.pmed.1001798

28.

Tily

Patridge

Cai

, et al. Gut microbiome activity contributes to prediction of individual variation in glycemic response in adults. Diabetes Ther. 2022;13(1):89-111. doi:10.1007/s13300-021-01174-z

29.

Korem

Zeevi

Zmora

, et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 2017;25(6):1243-1253. doi:10.1016/j.cmet.2017.05.002

30.

Zeevi

Korem

Zmora

, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. doi:10.1016/j.cell.2015.11.001

31.

Berry

Valdes

Drew

, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med. 2020;26(6):964-973. doi:10.1038/s41591-020-0934-0

32.

Vandeputte

. Personalized nutrition through the gut microbiota: current insights and future perspectives. Nutr Rev. 2020;78(Supplement_3):66-74. doi:10.1093/nutrit/nuaa098

33.

Franzosa

Huang

Meadow

, et al. Identifying personal microbiomes using metagenomic codes. Proc Natl Acad Sci USA. 2015;112(22):E2930-E2938. doi:10.1073/pnas.1423854112

34.

Rappaport

. Genetic factors are not the major causes of chronic diseases. PLoS One. 2016;11(4):e0154387. doi:10.1371/journal.pone.0154387

35.

Border

Johnson

Evans

, et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatr. 2019;176(5):376-387. doi:10.1176/appi.ajp.2018.18070881

36.

Strandwitz

. Neurotransmitter modulation by the gut microbiota. Brain Res. 2018;1693(Pt B):128-133. doi:10.1016/j.brainres.2018.03.015

37.

Org

Blum

Kasela

, et al. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort. Genome Biol. 2017;18(1):70. doi:10.1186/s13059-017-1194-2

38.

Parker

Fonseca

Carding

. Gut microbes and metabolites as modulators of blood-brain barrier integrity and brain health. Gut Microb. 2019;11(2):1-23. doi:10.1080/19490976.2019.1638722

39.

Wikoff

Anfora

Liu

, et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci USA. 2009;106(10):3698-3703. doi:10.1073/pnas.0812874106

40.

Visconti

Roy

CIL

Rosa

, et al. Interplay between the human gut microbiome and host metabolism. Nat Commun. 2019;10(1):4505. doi:10.1038/s41467-019-12476-z

41.

Kirk

Catal

Tekinerdogan

. Precision nutrition: a systematic literature review. Comput Biol Med. 2021;133:104365. doi:10.1016/j.compbiomed.2021.104365

42.

Spitzer

Kroenke

Williams

JBW

Löwe

. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-1097. doi:10.1001/archinte.166.10.1092

43.

Spitzer

Kroenke

Williams

JBW

. Group and the PHQPCS. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA. 1999;282(18):1737-1744. doi:10.1001/jama.282.18.1737

44.

Lacy

Patel

. Rome criteria and a diagnostic approach to irritable bowel syndrome. J Clin Medicine. 2017;6(11):99. doi:10.3390/jcm6110099

45.

Francis

Morris

Whorwell

. The irritable bowel severity scoring system: a simple method of monitoring irritable bowel syndrome and its progress. Aliment Pharm Therap. 1997;11(2):395-402. doi:10.1046/j.1365-2036.1997.142318000.x

46.

Kroenke

Spitzer

Williams

JBW

. The PHQ‐9. J Gen Intern Med. 2001;16(9):606-613. doi:10.1046/j.1525-1497.2001.016009606.x

47.

Sun

Mao

Wang

. The reliability and validity of PHQ-9 in patients with major depressive disorder in psychiatric hospital. BMC Psychiatr. 2020;20(1):474. doi:10.1186/s12888-020-02885-6

48.

Shen

Dimitrova

, et al. Gut microbiome activity predicts risk of type 2 diabetes and metformin control in a large human cohort. Medrxiv. Published Online; 2021. doi:10.1101/2021.08.13.21262051

49.

Hatch

Horne

Toma

, et al. A robust metatranscriptomic technology for population-scale studies of diet, gut microbiome, and human health. Int J Genom. 2019;2019:1718741. doi:10.1155/2019/1718741

50.

Toma

Duval

Pelle

, et al. A clinically validated human capillary blood transcriptome test for global systems biology studies. Biotechniques. 2020;69(4):289-301. doi:10.2144/btn-2020-0088

51.

Patridge

Eric

, et al. Microbial functional pathways based on metatranscriptomic profiling enable effective saliva-based health assessments for precision wellness. bioRxiv 2023. doi:10.1101/2023.11.01.565122. Submitted for publication.

52.

KEGG . KEGG Pathways; 2021. Published 2021. https://www.genome.jp/kegg-bin/show_pathway?map00650

53.

Viome . References for Viome functional scores; 2021 https://www.viome.com/referenceresults. Published 2021.

54.

Anand

Kaur

Mande

. Comparative in silico analysis of butyrate production pathways in gut commensals and pathogens. Front Microbiol. 2016;7. doi:10.3389/fmicb.2016.01945

55.

Vital

Howe

Tiedje

. Revealing the bacterial butyrate synthesis pathways by analyzing (Meta)genomic data. mBio. 2014;5(2). doi:10.1128/mbio.00889-14

56.

Konwar

Hanson

Pagé

Hallam

. MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinf. 2013;14(1):202. doi:10.1186/1471-2105-14-202

57.

Barreca

Nabavi

Sureda

, et al. Almonds (Prunus Dulcis mill. D. A. Webb): a source of nutrients and health-promoting compounds. Nutrients. 2020;12(3):672. doi:10.3390/nu12030672

58.

Hong

Zou

Jiang

, et al. Curcumin supplementation ameliorates bile cholesterol supersaturation in hamsters by modulating gut microbiota and cholesterol absorption. Nutrients. 2022;14(9):1828. doi:10.3390/nu14091828

59.

Wang

Zhu

Wang

. Choleretic activity of turmeric and its active ingredients. J Food Sci. 2016;81(7):H1800-H1806. doi:10.1111/1750-3841.13348

60.

Boonjaraspinyo

Boonmars

Aromdee

Puapairoj

. Indirect effect of a turmeric diet: enhanced bile duct proliferation in Syrian hamsters with a combination of partial obstruction by Opisthorchis viverrini infection and inflammation by N-nitrosodimethylamine administration. Parasitol Res. 2011;108(1):7-14. doi:10.1007/s00436-010-2031-7

61.

Zeng

Umar

Rust

Lazarova

Bordonaro

. Secondary bile acids and short chain fatty acids in the colon: a focus on colonic microbiome, cell proliferation, inflammation, and cancer. Int J Mol Sci. 2019;20(5):1214. doi:10.3390/ijms20051214

62.

Bernstein

Payne

Dvorakova

Garewal

. Bile acids as carcinogens in human gastrointestinal cancers. Mutat Res Rev Mutat Res. 2005;589(1):47-65. doi:10.1016/j.mrrev.2004.08.001

63.

Packer

Cadenas

. Lipoic acid: energy metabolism and redox regulation of transcription and cell signaling. J Clin Biochem Nutr. 2011;48(1):26-32. doi:10.3164/jcbn.11-005fr

64.

Oka

Parr

Barberio

Black

Savarino

Ford

. Global prevalence of irritable bowel syndrome according to Rome III or IV criteria: a systematic review and meta-analysis. Lancet Gastroenterology Hepatology. 2020;5(10):908-917. doi:10.1016/s2468-1253(20)30217-x

65.

Ettman

Abdalla

Cohen

Sampson

Vivier

Galea

. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw Open. 2020;3(9):e2019686. doi:10.1001/jamanetworkopen.2020.19686

66.

Bandelow

Michaelis

. Epidemiology of anxiety disorders in the 21st century. Dialogues Clin Neurosci. 2015;17(3):327-335. doi:10.31887/dcns.2015.17.3/bbandelow

67.

Napoleão

Fernandes

Miranda

Marum

. Effects of calorie restriction on health span and insulin resistance: classic calorie restriction diet vs. ketosis-inducing diet. Nutrients. 2021;13(4):1302. doi:10.3390/nu13041302

68.

Tricò

Moriconi

Berta

, et al. Effects of low-carbohydrate versus mediterranean diets on weight loss, glucose metabolism, insulin kinetics and β-cell function in morbidly obese individuals. Nutrients. 2021;13(4):1345. doi:10.3390/nu13041345

69.

Waliłko

Napierała

Bryśkiewicz

Fronczyk

Majkowska

. High-protein or low glycemic index diet—which energy-restricted diet is better to start a weight loss program? Nutrients. 2021;13(4):1086. doi:10.3390/nu13041086

70.

Kaliszewska

Allison

Martini

Arias

. The interaction of diet and mitochondrial dysfunction in aging and cognition. Int J Mol Sci. 2021;22(7):3574. doi:10.3390/ijms22073574

71.

Barber

Hanson

Kabisch

Pfeiffer

AFH

Weickert

. The low-carbohydrate diet: short-term metabolic efficacy versus longer-term limitations. Nutrients. 2021;13(4):1187. doi:10.3390/nu13041187

72.

Landry

Crimarco

Perelman

, et al. Adherence to ketogenic and mediterranean study diets in a crossover trial: the keto–med randomized trial. Nutrients. 2021;13(3):967. doi:10.3390/nu13030967

73.

Gomaa

. Human gut microbiota/microbiome in health and diseases: a review. Antonie Leeuwenhoek. 2020;113(12):2019-2040. doi:10.1007/s10482-020-01474-7

74.

Eisenstein

. The hunt for a healthy microbiome. Nature. 2020;577(7792):S6-S8. doi:10.1038/d41586-020-00193-3

75.

Benus

RFJ

Werf

TSd

Welling

, et al. Association between Faecalibacterium prausnitzii and dietary fibre in colonic fermentation in healthy human subjects. Brit J Nutr. 2010;104(5):693-700. doi:10.1017/s0007114510001030

76.

Louis

Flint

. Diversity, metabolism and microbial ecology of butyrate‐producing bacteria from the human large intestine. Fems Microbiol Lett. 2009;294(1):1-8. doi:10.1111/j.1574-6968.2009.01514.x

77.

Blount

. The unexhausted potential of E. coli. Elife. 2015;4:e05826. doi:10.7554/elife.05826

78.

Han

. Microbial siderophore enterobactin promotes mitochondrial iron uptake and development of the host via interaction with ATP synthase. Cell. 2018;175(2):571-582.e11. doi:10.1016/j.cell.2018.07.032

79.

Nguyen

Sperandio

. Enterohemorrhagic E. coli (EHEC) pathogenesis. Front Cell Infect Microbiol. 2012;2:90. doi:10.3389/fcimb.2012.00090

80.

Frame

Costa

Jackson

. Current explorations of nutrition and the gut microbiome: a comprehensive evaluation of the review literature. Nutr Rev. 2020;78(10):798-812. doi:10.1093/nutrit/nuz106

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