Biomarkers related to dietary behavior and uses thereof

Abstract

Disclosed herein are methods related to using wastewater samples to track dietary behavior at a population level, for example, to assess the prevalence of a plant-based diet in a population. Also disclosed herein are panels and kits related to tracking dietary behavior at a population level using wastewater samples.

Description

BIOMARKERS RELATED TO DIETARY BEHAVIOR AND USES THEREOFCROSS-REFERENCE TO RELATED APPLICATIONSFIELD OF THE INVENTION

[0001] This application claims the benefit of U.S. Provisional Application Number 63 / 509,748, filed June 22, 2023, titled “Biomarkers Related to Dietary Behavior and Uses Thereof,” the entirety of the disclosure of which is hereby incorporated by this reference.FIELD OF THE INVENTION

[0002] The invention relates to biomarkers and methods for assessing public health nutrition to elucidate population-level dietary behavior, for example, using wastewater-based epidemiology.BACKGROUND OF THE INVENTION

[0003] The impact of dietary behavior on chronic disease prevalence is undisputed, yet gaining a real-time understanding of this diet-disease relationship at the population level remains a challenge. Objectives have been set forth as part of the Healthy People 2030 Initiative by the United States Department of Health and Human Services to increase consumption of unprocessed whole-foods through evidence-based tactics, such as combining nutritional counseling with workplace or school nutrition policies; however, tools supportive of targeted and contextual educational interventions in public health nutrition are still lacking.

[0004] Understanding dietary behavior can become a complex endeavor. Population-level nutritional assessments offer beneficial insights into nutritional patterns and behavior; however, they often rely on survey -based methods subject to recall bias. Traditional methods for population-level nutritional assessment often rely on self-reported data, increasing risk of recall bias. While simultaneous use of multiple survey instruments can assist with this known limitation, it may also contribute to increased study costs. These limitations may impede overall progress on forming links between diet and an associated outcome, such as obesity or impacts of food insecurity, warranting alternative techniques.

[0005] This warrants the need for alternative methods that have the ability to determine population-level dietary information in real time to assess the need for and effectiveness of dietary interventions.SUMMARY OF THE INVENTION

[0006] Disclosed herein are chemical metabolite and microbiome biomarkers for monitoring dietary behavior in a population. Accordingly, also disclosed are methods of monitoring dietary behavior in a population, ranging in scale from a single dwelling, a hospital, a city block, a neighborhood, or an entire city. The chemical metabolite biomarkers described herein include genistein, daidzein, enterolactone, and equol. The microbiome biomarkers described herein include Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, Subdoligranulum, Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia.

[0007] In some embodiments, the biomarker panel for monitoring dietary behavior in a population comprises an agent that detects at least one chemical metabolite biomarker described above and at least one agent that detects the presence of at least one microbiome biomarker described above. In some aspects, the agent that detects at least one chemical metabolite biomarker is a labeled chemical metabolite (for example, the chemical metabolite with a radioactive label) use in a chromatographic assay. In some aspects, the agents that detects the presence of microorganisms are primers complementary to 16S rRNA of the microorganisms.

[0008] In some embodiments, the at least one agent that detects the presence of at least one microbiome biomarker detects at least one microorganism genus selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum. In some aspects, the biomarker panel comprises an agent that detects enterolactone and an assay that detects equol. In particular implementations, the biomarker panel further comprises at least one agent that detects the presence of Ruminococcus,- at least one agent that detects the presence of Blautia, at least one agent that detects the presence of Clostridium, ' at least one agent that detects the presence of Bifidobacterium, ' at least one agent that detects the presence of Romboutsia, at least one agent that detects the presence of Dorea,- and at least one agent that detects the presence of Subdoligranulum. In certain embodiments, such a biomarker panel further comprising at least one agent that detects the presence of at least one microorganism genus selected from the group consisting of: Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia.

[0009] In some embodiments, the biomarker panel further comprises at least one agent that detects the presence of at least one microorganism genus selected from the group consisting of: Faecalibacterium, Streptococcus, Akkermansia, and Lactobacillus.

[0010] In some implementations, a method of assessing the prevalence of a plant-based diet in a population comprises providing a first wastewater sample from the population; detecting the concentration of at least one chemical metabolite in the first wastewater sample; providing a second wastewater sample from the population, wherein the second wastewater sample is collected from a same location as the first wastewater sample and is collected at least two days after collecting the first wastewater sample; and detecting the concentration of at least one chemical metabolite in the second wastewater sample. The population is identified as having increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite is higher in the second wastewater sample than the first wastewater sample. In some aspects, the at least one chemical metabolite is selected from the group consisting of: genistein, daidzein, enterolactone, and equol. In certain implementations, the at least one chemical metabolite is enterolactone and / or equol.

[0011] Certain embodiments of this method further comprise detecting the concentration of at least one microorganism genus in the first wastewater sample and detecting the concentration of the at least one microorganism genus in the second wastewater sample. The population is identified as having the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus is higher in the second wastewater sample than the first wastewater sample. In some aspects, the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blaulia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum.

[0012] In some implementations, the method further comprises detecting the concentration of a second microorganism genus in the first and second wastewater samples. In such embodiments, the second microorganism genus is selected from at least one member in the group consisting of: Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. Detection of the second microorganism genus having a higher concentration in the second wastewater sample confirms the population has increased consumption of a plant-based diet.

[0013] In other implementations, the method of assessing the prevalence of a plant-based diet in a population comprises providing a first wastewater sample from the population; detecting the concentration of at least one microorganism genus in the first wastewater sample; providing a second wastewater sample from the population, wherein the second wastewater sample is collected from a same location as the first wastewater sample and is collected at least two days after collecting the first wastewater sample; and detecting the concentration of the at least onemicroorganism genus in the second wastewater sample. The population is identified as having increased consumption of a plant-based diet upon detection that the concentration of the at least one microorganism genus is higher in the second wastewater sample than the first wastewater sample. In some aspects, the at least one microorganism genus is selected from the group consisting of Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum .

[0014] In certain implementations, the method further comprises detecting the concentration of at least one chemical metabolite in the first wastewater sample and the second wastewater sample, wherein the at least one chemical metabolite is selected from the group consisting of genistein, daidzein, enterolactone, and equol. Detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus are higher in the second wastewater sample than the first wastewater sample identifies the population as having increased consumption of a plant-based diet.

[0015] The method may further comprise detecting the concentration of a second microorganism genus in the wastewater sample, wherein the second microorganism genus is selected from at least one member in the group consisting of Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. Increased consumption of a plant-based diet in the population is confirmed upon detection that the concentration of the second microorganism genus is higher in the second wastewater sample than the first wastewater sample.

[0016] In certain embodiments of the aforementioned methods, the concentration of the at least one chemical metabolite is detected using a combination of liquid chromatography and mass spectrometry. In particular implementations, the concentration of the at least one chemical metabolite is detected using liquid chromatography and tandem mass spectrometry. In certain embodiments, the concentration of the microorganism genus is detected by measuring the concentration of the 16S rRNA of the microorganism genus.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0018] FIGs. 1A-1F depict, in accordance with certain embodiments, phytoestrogen consumption from August 2017 through July 2019 all in mg d'1per capita. Thick dashed lines represent measured averages for each analyte or group of analytes in this study. Yellow shading represents literature reported estimated range of phytoestrogen consumption in a western society (1-3 mg d-1 per person). FIG. 1A shows the total phytoestrogen consumption (sum of genistein, daidzein, and enterolactone) with statistical comparisons for year by year (FIG. IB), weekday (Wk) versus weekend (Wkd) (FIG. 1C), seasons (Fall “F”, Winter “W”, Spring “Sp”, Summer “S”) (FIG. ID), isoflavone consumption (sum of genistein and daidzein) (FIG. IE), and lignan consumption indicated by the production of enterolactone (FIG. IF). Box plots show the 25th, 50th (median), and 75th quartiles with minimum and maximum error bars. Black diamond represent mean. Statistical significance tested using Mann Whitney U nonparametric test of variability with Benjamini Hochberg (BH) correction for false discovery (0.05) (*p<0.01).

[0019] FIG. 2 depicts, in accordance with certain embodiment, trends in total phytoestrogen consumption and / or metabolite production per month from August 2017 through July 2018 (thick grey dashed line) and August 2018 through July 2019 (black dotted line). Each month represents the average of the sum of genistein, daidzein, and enterolactone for each of the seven sample collection days. Results are shown in mg d'1per capita.

[0020] FIGs. 3A and 3B depict, in accordance with certain embodiments, measured average concentrations in raw wastewater ( g L'1), calculated mass loads incorporating flow estimates (g d'1), and per capita excretion ( g d'1per capita) for isoflavones genistein (FIG. 3A) and daidzein (FIG. 3B) for the entire study period beginning August 2017 through July 2019. Error bars represent standard deviation between duplicates (n = 2).

[0021] FIGs. 4A and 4B depict, in accordance with certain embodiments, phytoestrogen consumption shown separately for genistein, daidzein, and enterolactone. FIG. 4A show the per capita consumption and production shown in mg d'1per capita from August 2017 through July 2019. Box plots: center lines indicate the median; whiskers indicate the range (minimum, maximum); limits indicate 25thand 75thquartiles. FIG. 4B shows the Spearman’s rank order nonparametric results comparing measured concentration for each analyte ( g L'1) withrecorded daily ambient temperature for each sample collection day throughout this study. Tests resulted in weak associations and linearity (Spearman’s rank-order nonparametric test; p < 0.50) for genistein (p = 0.22; R2= 0.21; n = 321), daidzein ( = 0.16; R2= 0.31; n = 320), and enterolactone (p = 0.23; R2= 0.31; n = 316) indicating little influence of degradation.

[0022] FIGs. 5A and 5B depict, in accordance with certain embodiments, the measured concentrations in raw wastewater (pg L'1), calculated mass loads incorporating flow estimates (g d'1), and per capita excretion (pg d'1per capita) for enterolactone (August 2017 through July 2019) (FIG. 5A) and equol (January 2019 through July 2019) (FIG. 5B). Error bars represent standard deviation between duplicates (n = 2).

[0023] FIG. 6A depicts, in accordance with certain embodiments, the population-scale daidzein consumption (light blue diamonds) and equol metabolite release per active equol shedder (30% of population; shown as white squares) observed in an urban population of -10,000 people using wastewater analysis. FIG. 6B depicts, in accordance with certain embodiments, paired observations (n = 49) of concentrations of daidzein and equol in community wastewater (pg L'1) showed a strong linear relationship (R2= 0.80) and strong positive association (p = 0.84) using Spearman’s rank-order nonparametric correlation test.

[0024] FIG. 7 depicts, in accordance with certain embodiments, a case study-derived conceptual framework for integrating WBE into population-level dietary assessments.

[0025] FIGs. 8A-8C depict, in accordance with certain embodiments, microbial composition of wastewater collected in this study between January and December 2018. FIG. 8A shows the relative abundance of select bacterial genera involved in phytoestrogen metabolism within the human gut. FIG. 8B depicts the copy number of bacterial 16S rRNA genes per liter determined using quantitative polymerase chain reaction (qPCR). FIG. 8C shows the average semi- quantitative (SQ) abundances (gene copies L'1) of the same phytoestrogen-metabolizing genera shown with total phytoestrogen consumption (mg d'1per person) per month throughout 2018.

[0026] FIGs. 9A-9C depict, in accordance with certain embodiments, the relationship of phytoestrogen and alcohol consumption in an urban study population as per capita mass loads detected in wastewater (mg d'1per person) for equol (grey) (FIG. 9 A) and ethyl sulfate (green) (FIG. 9B) from January through April 2019, and in March 2019 (FIG. 9C) showing increases over the St. Patrick’s Day Holiday weekend. Statistical comparison suggests strong positive relationship between equol and ethyl sulfate (p = 0.89; p < 0.001) confirmed by Spearman’s rank-order nonparametric test.DESCRIPTION OF THE INVENTION

[0027] Detailed aspects and applications of the invention are described below in the drawings and detailed description of the invention. Unless specifically noted, it is intended that the words and phrases in the specification and the claims be given their plain, ordinary, and accustomed meaning to those of ordinary skill in the applicable arts.

[0028] In the following description, and for the purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the various aspects of the invention. It will be understood, however, by those skilled in the relevant arts, that the present invention may be practiced without these specific details. It should be noted that there are many different and alternative configurations, devices, and technologies to which the disclosed inventions may be applied. The full scope of the inventions is not limited to the examples that are described below.

[0029] The singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a step” includes reference to one or more of such steps.

[0030] As used herein, the term “panel” refers to a composition, such as an array or a collection, comprising one or more biomarkers. The term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values. In one embodiment, the number of biomarkers useful for a biomarker subset or panel is based on the sensitivity and specificity value for the particular combination of biomarker values.

[0031] The terms “sensitivity” and “specificity” are used herein with respect to the ability to correctly classify a population, based on one or more biomarker values detected in the wastewater sample, as having versus not having increased consumption of a plant-based diet. Sensitivity indicates the performance of the biomarker(s) with respect to correctly classifying populations that have increased consumption of a plant-based diet, while specificity indicates the performance of the biomarker(s) with respect to correctly classifying populations that do not have increased consumption of a plant-based diet. Selection of the desired number of such biomarkers depends on the specific combination of biomarkers chosen.

[0032] As used herein, the term “wastewater sample” includes to a liquid sample collected from a wastewater treatment plant (for example, wastewater treatment plant influent or primary sludge) as well as liquid sample collected from toilets of a household or building, includingfrom a sewer collection system or sewer infrastructure. In some aspects, a wastewater sample contains human urine waste.

[0033] A plant-based, whole-foods diet generally consists of consuming predominantly vegetables, fruits, nuts, and seeds, with minimal to no consumption of animal-based food products. This type of diet has been shown to prevent, minimize, or even reverse symptoms of certain chronic diseases, including type 2 diabetes and cardiovascular disease, which has motivated campaigns to promote plant-based dietary behavior in the United State in recent years. Naturally-occurring, plant-derived chemicals known as phytoestrogens can serve as biomarkers indicative of a plant-based diet as they are consumed through a wide variety of vegetables (cauliflower, broccoli), fruits (berries, apples), nuts (pistachios, cashews), seeds (flax, sunflower), beans and legumes (soy, lima), and grains (oat, wheat). Two major classes of phytoestrogens in a Western-type diet include isoflavones, largely representing consumption of soy-based foods and food products, as well as lignans which are much more ubiquitous. These two classes have been investigated in individualized human biomonitoring studies utilizing techniques such as targeted metabolomics to measure phytoestrogen-associated chemicals genistein, daidzein, enterolactone, and equol, coupled with various genomic analyses to assess human gut microbial interactions in response to phytoestrogen consumption. Due to their ability to interfere with endogenous human estrogen, phytoestrogen intake has been hypothesized to be associated with increased risk of breast cancer. However, available data are unsupportive, with phytoestrogen consumption in Asia exceeding those in the U.S. by an order of magnitude (25-50 vs. 1-3 mg d'1), while breast cancer prevalence in Asia remains much lower than the U.S. These findings in part could be attributed to differences in human gut microbial communities that produce phytoestrogen-derived metabolites, potentially offering a multitude of benefits for the human host. For example, increases in the production of lignan- and isoflavone-derived gut microbial products, enterolactone and equol, have been linked to cancer- and / or cardiovascular-protective properties. Bacterial genera belonging to the human gut microbiome and reported to participate in these enterolactone- and equol -producing, phytoestrogen-derived pathways include Ruminococcus, Slackia, and Bacteroides, rendering these and related commensals prime investigative targets when studying plant-based diets.

[0034] Wastewater-based epidemiology (WBE) is a rapidly evolving scientific discipline that leverages the analysis of biochemical signatures detectable in community wastewater conveying composited human excreta (i.e., urine, stool), to offer insights into population-level health, behavior, exposure, and activity. Historically employed primarily to monitor illicit drug use across cities, WBE recently has gained widescale attention and acceptance as a companiontool to random clinical testing for monitoring the COVID-19 pandemic at multiple spatial and geographic scales (i.e., country-, city-, neighborhood-, and / or building-level) to determine community-level infection. The transcendence of WBE from drug use surveillance to viral monitoring with subsequent dissemination of actionable population health data throughout the pandemic has provided a powerful impetus to also leverage the potential of this methodology for other public health fields.

[0035] Wastewater-based epidemiology (WBE) offers a unique perspective on populationlevel human behavior, activity, and exposure, and has been proposed as a viable tool for dietary assessment due to its inclusive, objective, and near real-time capabilities. In particular, repeated proposals for using WBE to elucidate dietary behavior has resulted in only one experimental report which did confirm the feasibility and utility of monitoring the phytoestrogen chemicals genistein, daidzein, and enterolactone to evaluate plant-based trends in populations by WBE. However, the complex relationship between dietary intake and subsequent metabolism by both the human host and its gut microbiome warrants further multi-pronged analyses along the path to the successful deployment of WBE for advanced longitudinal, high-frequency, high- resolution (sub-sewershed) dietary population health assessment.

[0036] Further, instrumentation commonly used for chemical analysis by WBE to understand community-level metabolomics has largely involved liquid chromatography-tandem mass spectrometry (LC-MS / MS), while biological analyses has leveraged genomic analyses including quantitative PCR (qPCR, RT-qPCR) or sequencing (shotgun, next generation, etc.) in response to the COVID-19 pandemic. A new frontier for WBE is the recruitment of proteomics in order to continue to advance the field and inform on other unique aspects of human health, behavior, exposure, and activity.

[0037] As many WBE studies are commonly performed on a longitudinal basis, with sample collection occurring at a regular cadence, this method can ultimately serve to complement the traditional medical model of ‘grab sampling’ individual patients, as these time-discrete samples only reflect the health status at one point in time from a single person. Thus, a great benefit to WBE is the ability to exhibit trends over time of all individuals within a given community, not just those who are able to seek regular health screenings by a physician, offering unique insights that would then prompt relevant public health interventions when necessary; ultimately adopting a diagnostic approach for public health.

[0038] While WBE has been identified here to potentially represent a powerful tool in the tracking and monitoring of global deadly diseases, there are certain limitations that should be considered. For example, endogenous biomarkers are commonly excreted in much lowerconcentrations, rendering detection and confident quantification more challenging. Mixing of urine with stool also injects uncertainty as to the absolute quantity of biomarkers present and makes the detection of urinary biomarkers more difficult due to increased matrix effects of fecal matter. Furthermore, biomarkers may be fairly stable in urine but much more susceptible to degradation or transformation within the sewerage system, due to the presence of fecal microbiomes and active biofilms covering the inner linings of sewer pipes.

[0039] As reported herein, a two-year case study (August 2017 through July 2019) integrating a multidisciplinary approach was conducted in order to (z) investigate trends of indicators of a plant-based diet (i.e., phytoestrogens), (zz) evaluate novel parent-metabolite relationships, and (Hi as a proof-of-concept, assess feasibility of analyzing human gut microbial composition at population-scale. This study yielded for the first time, corresponding data sets on phytoestrogen dietary trends as a proxy for plant-based diets and the gut microbiome of local human populations. For all compounds examined, there was a significant increase in consumption starting in January, suggestive of temporal albeit un-sustained lifestyle changes associated with individual New Years’ resolutions of the study population, such as to increase intake of plantbased foods (FIGs. 1A-1F) This study represents a first successful demonstration of longitudinal investigation showcasing continuous signal detectability, considerable dynamic range, and joint trending of wastewater-borne biomarkers that are modulated as a function of human diet and behavior.

[0040] As a result of the study, a generic framework for integrating WBE evaluations into an assessment of dietary behavior was developed. The results identified multi-parameter datasets for an urban sub-population showing the interplay between human behavior (e.g., alcohol consumption on holidays; short-term healthier dietary choices after New Year’s Day) and dietary indicators detectable in composited municipal wastewater; an association between wastewater-borne levels of phytoestrogens and related gut microbial composition at the community level; novel use of equol, a microbial metabolite of daidzein, to provide a more nuanced interpretation of wastewater-derived consumption measurements of isoflavones; and relationships between simultaneously consumed foods or food products that may serve as driving factors for subsequent production of microbially-derived metabolites. Accordingly, disclosed herein are chemical metabolite and microbiome biomarkers for population-level assessment of dietary behavior and wastewater-based methods of tracking population dietary behavior, in particular the prevalence of a plant-based diet in a population.

[0041] Among the major benefits of WBE versus analysis of urine and stool from individuals are superior convenience, repeatability, and lower costs. A cost analysis performed in thepresent work yielded per-person costs of < $1 for a WBE study (10,000 population) with existing startup equipment or < $2 per person, if capital expenses are included (Table 1). This is considerably less than the estimated average cost for traditional nutritional assessment, which is in the range of >$15 per person and depends on the instrument of analysis employed.Table 1. Cost estimate of conducting a WBE study for 24-months at one locationBiomarker Panel

[0042] The chemical metabolite and microbiome biomarker panel described herein relate to methods of tracking population dietary behavior. The biomarkers detected by biomarker panel include at least one chemical metabolite biomarker and / or at least one microbiome biomarker.

[0043] The at least one chemical metabolite biomarker is selected from the group consisting of: genistein, daidzein, enterolactone, and equol. In some aspects, the chemical metabolite biomarker in the biomarker panel may further include ethyl sulfate. Accordingly, in some embodiments, the biomarker panel is a kit comprising assays or chemical probes that detect the chemical metabolite biomarkers so that the concentration of the chemical metabolite biomarker in a sample can be measured.

[0044] Thus, in some aspects, the biomarker panel comprises an assay or chemical probe that detects or measures genistein, an assay or chemical probe that detects or measures daidzein, an assay or chemical probe that detects or measures enterolactone and / or, an assay or chemical probe that detects or measures equol. In some implementations, the biomarker panel further comprises an assay or chemical probe that detects or measures ethyl sulfate. In certain embodiments, the biomarker panel comprises an assay or chemical probe that detects or measures enterolactone and an assay or chemical probe that detects or measures equol.

[0045] In particular implementations, the assays that measure the chemical metabolite biomarkers is uses chromatographic assaying, for example, liquid chromatography (LC); gas chromatography (GC), mass spectrometry (MS), or a combination thereof. In some embodiments, the chemical metabolite is measured by LC / MS or GC / MS. Accordingly, in certain embodiments, the biomarker panel comprises labeled chemical metabolite biomarkers,which would be used to generate a standard curve that facilitates the measurement of the chemical metabolite biomarker in the sample.

[0046] The at least one microbiome biomarker is at least one microorganism genus selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, Subdoligranulum, Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. In some embodiments, the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum. In some aspects, such a biomarker panel may further detect at least one microbiome biomarker selected from at least one microorganism genus in the group consisting of: Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. In certain implementations, the biomarker panel may further detect at least one microbiome biomarker selected from at least one microorganism genus in the group consisting of: Faecalibacterium, Streptococcus, Akkermansia, and Lactobacillus.

[0047] In some aspects, the biomarker panel detects at least one biomarker, at least two biomarkers, or at least three biomarkers. For example, the biomarker panel detects a chemical metabolite and a microbiome biomarker, one chemical metabolite and two microbiome biomarkers, two chemical metabolites and at least two microbiome biomarkers, or three chemical metabolites and at least three microbiome biomarkers. In some implementations, the biomarker panel comprises an agent that detects the microorganism genus(es) of the at least one microbiome biomarker. In some implementations, the agent that detects the microbiome biomarker is a primer for detecting 16S rRNA of the corresponding microorganism genus, for example, Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, or Subdoligranulum. In particular implementations, the agent that detects the microbiome biomarker is a pair of primer for detecting 16S rRNA of the corresponding microorganism genus. The specific sequence of primers that detect the 16S rRNA of the contemplated microbiome biomarkers can be determined or optimized according to methods described in the prior art.

[0048] In some aspects, the biomarker panel comprises an assay or chemical probe that detects enterolactone and an assay or chemical probe that detects equol. In particular implementations, the biomarker panel further comprises at least one agent that detects the presence of Ruminococcus,- at least one agent that detects the presence of Blautia, at least one agent that detects the presence of Clostridium,- at least one agent that detects the presence of Bifidobacterium,' at least one agent that detects the presence of Romboutsia, at least one agentthat detects the presence of Dorea, and at least one agent that detects the presence of Subdoligranulum. In certain embodiments, such a biomarker panel further comprising at least one agent that detects the presence of at least one microorganism genus selected from the group consisting of: Slackia. Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. In some embodiments, the biomarker panel further comprises at least one agent that detects the presence of at least one microorganism genus selected from the group consisting of: Faecalibacterium, Streptococcus, Akkermansia, and Lactobacillus.Methods of Use

[0049] Also disclosed herein are methods of monitoring the prevalence of a plant-based diet in a population using a wastewater sample, for example sub-sewershed wastewater sample.

[0050] The method of assessing the prevalence of a plant-based diet in a population comprises providing a first wastewater sample from the population; detecting the concentration of at least one chemical metabolite in the first wastewater sample; providing a second wastewater sample from the population, wherein the second wastewater sample is collected from a same location as the first wastewater sample and is collected at least two days after collecting the first wastewater sample; and detecting the concentration of at least one chemical metabolite in the second wastewater sample. The population is identified as having increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite is higher in the second wastewater sample than the first wastewater sample. In some aspects, the at least one chemical metabolite is selected from the group consisting of: genistein, daidzein, enterolactone, and equol. In certain implementations, the at least one chemical metabolite is enterolactone and / or equol.

[0051] Certain embodiments of this method further comprise detecting the concentration of at least one microorganism genus in the first wastewater sample and detecting the concentration of the at least one microorganism genus in the second wastewater sample. The population is identified as having the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus is higher in the second wastewater sample than the first wastewater sample. In some aspects, the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum.

[0052] In some implementations, the method further comprises detecting the concentration of a second microorganism genus in the first and second wastewater samples. In suchembodiments, the second microorganism genus is selected from at least one member in the group consisting of: Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. Detection of the second microorganism genus having a higher concentration in the second wastewater sample confirms the population has increased consumption of a plant-based diet.

[0053] In other implementations, the method of assessing the prevalence of a plant-based diet in a population comprises providing a first wastewater sample from the population; detecting the concentration of at least one microorganism genus in the first wastewater sample; providing a second wastewater sample from the population, wherein the second wastewater sample is collected from a same location as the first wastewater sample and is collected at least two days after collecting the first wastewater sample; and detecting the concentration of the at least one microorganism genus in the second wastewater sample. The population is identified as having increased consumption of a plant-based diet upon detection that the concentration of the at least one microorganism genus is higher in the second wastewater sample than the first wastewater sample. In some aspects, the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blaulia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum .

[0054] In certain implementations, the method further comprises detecting the concentration of at least one chemical metabolite in the first wastewater sample and the second wastewater sample, wherein the at least one chemical metabolite is selected from the group consisting of: genistein, daidzein, enterolactone, and equol. Detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus are higher in the second wastewater sample than the first wastewater sample identifies the population as having increased consumption of a plant-based diet.

[0055] The method may further comprise detecting the concentration of a second microorganism genus in the wastewater sample, wherein the second microorganism genus is selected from at least one member in the group consisting of: Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter , Prevotella, and Senegalimassilia. Increased consumption of a plant-based diet in the population is confirmed upon detection that the concentration of the second microorganism genus is higher in the second wastewater sample than the first wastewater sample.

[0056] In some implementations, the concentration of the at least one chemical metabolite biomarker is detected using a chromatographic assay, for example, liquid chromatography (LC); gas chromatography (GC), mass spectrometry (MS), or a combination thereof. In someembodiments, the chemical metabolite is measured by LC / MS or GC / MS. In particular embodiments, the concentration of the at least one chemical metabolite biomarker is detected using a combination of liquid chromatography (LC) with mass spectrometry (MS). In certain embodiments, the concentration of the microorganism genus is detected by measuring the concentration of the 16S rRNA of the microorganism genus. In some aspects, the concentration of 16S rRNA of the microorganism genus is measured using a primer with a nucleotide sequence that is complementary to the 16S rRNA of the microorganism genus. For example, the concentration of 16S rRNA of the microorganism genus is measured using pair of primer that bind to the 16S rRNA of the microorganism genus.Examples

[0057] The invention is further illustrated by the following examples that should not be construed as limiting. The contents of all references, patents, and published patent applications cited throughout this application, as well as the Figures, are incorporated herein by reference in their entirety for all purposes.L Study location and samples analyzed

[0058] This study took place in a small (-10,000 population) sub-sewer catchment within a large urban city located immediately south-east to a major public university. This wastewater catchment studied is predominantly residential, comprised of a mix of single-family homes, condominiums, apartment complexes, and off-campus student housing. Demographic estimates based on self-reported data for this catchment area are as follows: White NonHispanic (40%), Asian (25%), Hispanic or Latino (21%), African American (7%), and American Indian / Alaska Natives (4%).

[0059] Composited wastewater samples were collected for seven consecutive days per month from August 2017 through July 2019, resulting in a total of 156 samples collected, processed, and analyzed, and 100% detection frequency for all chemical markers of phytoestrogens under investigation. The study utilized sub-sewershed monitoring to reduce potential in-sewer loss of analytes during the time required for the analyte to travel in the pipe from the point of human excretion to the sampling location.Cochran ’s formula to assess representative sampling approach for WBE z pq no=^~Where: z = 1.645 (90% CI) p = 0.5 q = i-p e = 0.101.6452(0.5)(0.5) n° ~ (UO2n0= 67.65Adjusted to population size: n0n = - l+no ~11 +NWhere: n = adjusted sample sizeN = population size (365 days in one year)67.65 n ~ ~ 67.64 - 1 1+~ !65— n = 57.2 days needed to represent the yearII. Per capita chemical measurements in community wastewater

[0060] In the first year of this neighborhood-level monitoring study (August 2017 through July2018), total phytoestrogen consumption patterns (defined as the sum of genistein, daidzein, and enterolactone) displayed a distinct increase at the start of each new year (January) with a gradual subsequent decline through March. In the following year (August 2018 through July2019), this pattern was repeated, with average overall per capita consumption in year two (5.0 ± 2.3 mg d'1) greater than year one (4.1 ± 2.2 mg d'1) (FIGs. 1A and 2). While both yearly measurements for total phytoestrogen consumption were higher than the estimated average for the U.S. by traditional methods (1-3 mg d'1per capita), individual isoflavone and lignan consumption fell within this expected range: isoflavones 2.7 ± 1.5 (Year 1) 3.5 ± 1.6 (Year 2); lignan 1.6 ± 0.9 (Year 1) 2.1 ± 1.0 (Year 2) (FIGs. IE and IF). Measured daily concentrations in raw wastewater, calculated mass loads incorporating flow, and estimated average per-capitaexcretion are shown for genistein, daidzein, and enterolactone in FIGs. 3A and 3B. Statistical comparisons revealed significant differences (p 0.01) between Years 1 and 2 (FIG. IB) as well as seasonal changes between Fall and Winter, and Spring and Summer (FIG. ID). While phytoestrogen consumption was lower overall on weekends versus weekdays, these differences were subtle and not statistically significant (p > 0.05) (FIG. 1C). Results of all statistical comparisons for changes in dietary behavior are listed in Table 1. An assessment of the potential for temperature-dependent in-sewer decay of analytical targets suggested short-term stability and only a weak susceptibility to degradation for genistein (p = 0.22), daidzein (p = 0.16), and enterolactone (p = 0.23) (FIG. 4), implying that the reported wastewater-derived, phytoestrogen-metabolite loads are a reasonable reflection of human excretion activity and, by extension, human dietary behavior.

[0061] Consumption of phytoestrogens can vary depending on life stage, dietary preferences, demographics, and / or regular access to healthy fruits and vegetables. Thus, the reported estimate of the U.S. average may not be entirely applicable to this small population of -10,000 people with a distinct demographic profile residing in the study sewershed. For example, according to the U.S. Census Bureau, demographics across the nation consist of 60% White Non-Hispanic, 5% Asian, 18% Hispanic or Latino, 13% Black, and 1% American Indian / Alaska Native, whereas in this study, White Non-Hispanic is 40%, with 25% Asian, 21% Hispanic or Latino, 7% Black, and 4% American Indian / Alaska Native. This difference in demographic distribution could result in the disparity between the estimated U.S. consumption rate for phytoestrogens, and what was actually measured herein by using this innovative WBE approach. Measured isoflavone and lignan consumption in the study population informed by wastewater, however, was consistently within this 1-3 mg d'1estimated range, although isoflavones were closer to the 3 mg d'1ceiling value (FIG. IE), which could be attributed to the greater percentage of Asian residents within this catchment who have been reported to consume a much greater amount of isoflavones than those in westernized cultures. These differences further demonstrate the importance of performing more contextually- relevant population-level measurements that are indicative of the population sample under study in order to develop meaningful intervention strategies when indicated. a. Methods for measuring phytoestrogensTable 2. Mass spectrometry transitions for measuring phytoestrogens. *Parent ion > Quantification ion, Confirmation ion; DP: Declustering potential; EP: Entrance Potential;CE: Collision Energy;Analyte MS / MDaidzein 252.7 > 132.3, 90.7 50 -100 / -100 -10 / -10 -54Z-54 -9 / -1Genistein 268.6 > 132.7, 62.7 50 -90 / -90 -10 / -10 -44 / -60 -51-9Enterolactone 297.0 > 252.9, 106.6 50 -80 / -80 -10 / -10 -28 / -40 -5 / -17Genistein-d4 272.7 > 136.9, 62.8 150 -80 -10 -44 -5Equol 241.1 > 120.8, 118.7 150 -551-55 -10 / -10 -20 / -30 -9 / -5Table 3. Recoveries and method detection limits (MDLs) of phytoestrogens under investigation in this study. *l)aidzein, genistein, and enterolactone previously validated.Table 4. Correction factors and elimination half-lives (hours) for estimating phytoestrogen consumption.III, Evaluation of precursor to metabolite relationships

[0062] The isoflavones investigated in this study, daidzein and genistein, are both precursor compounds (i.e., analytes naturally found in plants that are part of the human diet), and thus it was necessary to test the efficacy of using these compounds as indicators of phytoestrogen consumption by incorporating a human excreted metabolite of isoflavones. Equol, a human gut microbial metabolite of the isoflavone, daidzein, was chosen for this purpose and integrated into the study starting in January of 2019 (through July 2019; n = 49 observations). Detailed daily concentrations, calculated mass loads incorporating wastewater-fl ow measurements, andper-capita excretion are listed in FIG. 5B. Trends observed here for equol aligned with those of the other phytoestrogens investigated in this study; elevated phytoestrogen consumption was found at the beginning of the year, with a subsequent decline after March. Statistical analysis revealed a strong positive correlation between daidzein and equol (p = 0.84), with a precursor- to-metabolite ratio of 0.2 (FIG. 6B). Equol production varies by person and is estimated to occur in approximately 30% of people in westernized societies. Thus, average per-capita estimates of equol is much lower than the actual equol excreted by equol -producing individuals within a given population. Equol production therefore can be reported in two different ways; as a population average and as the actual output per equol -producer depending on the study population (FIGs. 6A and 6B). Once adjusted for the population-specific producer to nonproducer ratio, computed normalized values are directly comparable and transferable between studies and different demographics.

[0063] The chemical measurements reported herein are slightly higher than those found in the only prior WBE study measuring genistein, daidzein, and enterolactone in monthly composites of raw wastewater entering a local treatment plant to assess diet. There are significant differences in study design between this prior study and reported herein that should be noted. In this study, we collected wastewater as 24-hour composite samples obtained daily for seven consecutive days from within the community sewer infrastructure followed by same-day processing and analysis. The previous study collected one single twenty-four-hour composite sample per month from a wastewater treatment plant, with subsequent shipment prior to sample processing and analysis. Shorter hydraulic residence times and a rapid analysis of samples without delay likely contributed to the comparatively stronger signal observed in the present work.

[0064] Table 5. Calculated p-values using non-parametric Mann Whitney U Test with false discovery rate (0.05) corrections using Benjamini -Hochberg Procedure (BH) to test for statistical significance between months, both years of study, weekday versus weekend, and seasonal variability. Statistically significant values noted as (*) for p < 0.05 or (f) for p < 0.01 Abbreviations: Gen: Genistein; Daid: Daidzein; Ent: Enterolactone; Eq: Equol; Yl: August 2017- July 2018; Y2: August 2018-July 2019; Wk: Weekday (Monday - Friday); Wnd: Weekend (Saturday, Sunday); F: Fall; W: Winter; Sp: Spring; S: Summer.IV. Population-level human gut microbial interactions associated with diet

[0065] It is evident that human gut microbial interactions that occur in response to plant-based food consumption may hold significant benefits for the human host. Evidence suggests an increase in regular consumption of plant-based foods can serve to promote gut microbial growth amenable for production of enterolactone and equol, which offer protection against certain nutrition-related chronic disease. While it is currently understood that approximately 30% of westernized regions are able to produce equol, a population-level investigation could provide some deeper insight into this statistic.

[0066] While slightly lower than 30%, a daidzein-to-equol (precursor-to-metabolite) ratio of 0.2 (20%) was observed in this study, thus, wastewater-acquired measurements were in-line with universal population production estimates using more conventional methods. Specifically for enterolactone, studies relying on urinary analysis of individuals reported a noted increase in production to be associated with protective properties toward cancer and cardiovascular disease, with use of oral antibiotics shown to decrease production, indicating the measuredvalues are predominantly driven by gut microbes. Studies have also found positive associations between intake of isoflavone-rich foods and subsequent urinary elevations in equol production, thereby affirming the role of diet in promoting growth of health-promoting commensal microbes. Following a predominantly vegetarian or vegan diet has also been shown to strongly correlate with increased production of particular bacterial genera responsible for producing enterolactone and equol, including Ruminococcus, Eubacterium, Slackia, Bacteroides, and Bifidobacterium .

[0067] As a proof of concept for investigating human gut microbial mechanisms at the population-scale, a subset of samples (n = 84; January through December 2018) was tested to determine if the bacterial genera identified to be involved in phytoestrogen metabolism from individualized studies are also observable at the community level via analysis of composited human waste extant in municipal wastewater. While microbial communities have been assessed in community sewage to understand overall human health, this approach to investigating dietary behavior using WBE has yet to be reported, and thus serves as a foundational framework for future analysis of population nutritional assessment as the field of WBE continues to advance (FIG. 7).

[0068] Compositional analysis performed monthly down to the genus level revealed the presence of specific bacterial taxa known to be associated with the human gut microbiome, such as Bifidobacterium, Blaulia, and Romboulsia, were detectable in a community sewage sample (FIG. 8A). A detection frequency of 100% in all samples was found for Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum, followed by Intestinibacter (66%), Eubacterium (58%), Bacteroides (50%), Prevotella (25%), Senegalimassilia and Roseburia (16%), and Slackia (8%). Measurements of 16S rRNA gene copies L'1provided an indication of bacterial concentration in each wastewater sample, ranging from 4.3 x 1011to 2.4 x 1012(FIG. 8B). Using the measured relative abundance (%) and bacterial concentration estimates, average semi-quantitative abundances of the number of 16S rRNA genes belonging to select enterolactone- and equol-producing genera were calculated, with resultant values ranging from 9.0 x 106(Eubacterium,' June) to 2.6 x 1011(Bifidobacterium,' October). Estimated average monthly phytoestrogen consumption, determined from chemical biomarker measurements, are shown alongside average semi- quantitative abundances of population-level phytoestrogen-metabolizing taxa within the human gut microbiome to enable a delineation of trends. Elevated trends in both sets of data were observed at the beginning of the year (2018) and again towards the end of the summerheading into the fall (autumn), displaying a weak and non-statistically significant association (p = 0.24, p > 0.05)), however, intriguing (FIG. 8C).

[0069] Table 6. Comparison of phytoestrogen-metabolizing human gut microbial genera reported at individual-level with the observed population-level genera in this study. *Population-level detected in community municipal wastewater **Individual-level detected in human fecesV. Evaluating interactions between simultaneously consumed foods and metabolite production

[0070] In a typical human diet, multiple types of foods and food products are consumed simultaneously, including alcoholic beverages during certain mealtimes or celebrations. A sharp increase in equol production was observed in March of 2019 (pg d'1per person); 207 ± 7.1 from Saturday, March 16thto 594 ± 65.1 on Sunday, March 17th, the St. Patrick’s Day holiday. The notable spike in equol production during the St. Patrick’s Day holiday period in March of 2019 could be from widescale, collective participation in consumption of traditional Irish foods in celebration of the holiday, such as colcannon, fish and chips, or corned beef and cabbage, which all contain varied amounts of phytoestrogens, however, it may more likely bedue to an increase in acute alcohol consumption. Given the nature of the holiday where copious amounts of alcohol are consumed in a relatively short period of time, it could be a viable explanation for these measured spikes in phytoestrogen consumption. Interestingly, in an earlier study comparing equol -producers and non-equol-producers, those who reported greater alcohol consumption were more likely to be much stronger equol producers; however, the mechanism behind this is still unclear. This prompted an additional investigation into potential interactions with other human consumption behaviors during this particular holiday, namely alcohol, and its influence on subsequent production of equol at the population scale (FIGs. 9A- 9C)

[0071] Calculated population normalized mass loads (mg d'1per person) of equol and ethyl sulfate, a metabolite of alcohol, were compared from January through April 2019 to assess overall trends, capture holiday behavioral anomalies, and seek out concordances in the trending of the various analytes. The highest values for ethyl sulfate within this timeframe occurred over the St. Patrick’s Day holiday: Saturday, March 16th(13.6 ± 1.5 mg d'1per person) and Sunday, March 17th( 13.5 ± 1.5 mg d'1per person), indicating a pointed increase in alcohol consumption that strongly correlates with the increase observed in equol production (p = 0.89). Thus, FIGs. 9A-9C show strong positive correlations between ethyl sulfate, an alcohol metabolite commonly used in WBE studies to indicate alcohol use and equol. This result suggests that alcohol consumption may serve as a driving factor behind equol production by the human gut microbiome, when consumed in high amounts and within a condensed timeframe.

Claims

CLAIMSWhat is claimed is:

1. A method of assessing the prevalence of a plant-based diet in a population comprising: providing a first wastewater sample from the population; detecting the concentration of at least one chemical metabolite in the first wastewater sample, wherein the first wastewater sample is collected from a location and the at least one chemical metabolite is selected from the group consisting of: genistein, daidzein, enterolactone, and equol; providing a second wastewater sample from the population, wherein the second wastewater sample is collected from the same location as the first wastewater sample and is collected at least two days after collecting the first wastewater sample; detecting the concentration of at least one chemical metabolite in the second wastewater sample; and identifying the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite is higher in the second wastewater sample than the first wastewater sample.

2. The method of claim 1, wherein the concentration of the at least one chemical metabolite is detected using liquid chromatography (LC) with mass spectrometry (MS) or gas chromatography (GC) with mass spectrometry (MS).

3. The method of claim 2, wherein the concentration of the at least one chemical metabolite is detected using liquid chromatography with tandem mass spectrometry.

4. The method of any one of claims 1-3, wherein the at least one chemical metabolite is enterolactone and equol.

5. The method of any one of claims 1-3, further comprising: detecting the concentration of at least one microorganism genus in the first wastewater sample, wherein the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, Subdoligranulum, Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter, Prevotella, and Senegalimassilia, anddetecting the concentration of the at least one microorganism genus in the second wastewater sample; wherein the population is identified as having the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus are higher in the second wastewater sample than the first wastewater sample.

6. The method of claim 4, wherein the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum.

7. The method of claim 5, wherein the concentration of the at least one microorganism genus is detected using a primer complementary to the 16S rRNA of the of the at least one microorganism genus.

8. The method of claim 4, further comprising: detecting the concentration of at least one microorganism genus in the first wastewater sample, wherein the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, Subdoligranulum, Slackia, Bacteroides, Eubacterium, Roseburia, Intestinibacter, Prevotella, and Senegalimassilia, and detecting the concentration of the at least one microorganism genus in the second wastewater sample; wherein the population is identified as having the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus are higher in the second wastewater sample than the first wastewater sample.

9. The method of claim 8, wherein the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulum.

10. The method of claim 8, wherein the concentration of the at least one microorganism genus is detected by measuring the concentration of the 16S rRNA of the at least one microorganism genus.

11. A method of assessing the prevalence of a plant-based diet in a population comprising: providing a first wastewater sample from the population; detecting the concentration of at least one microorganism genus in the first wastewater sample, wherein the first wastewater sample is collected from a location and the at least one microorganism genus is selected from the group consisting of: Ruminococcus, Blautia, Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulunr, providing a second wastewater sample from the population, wherein the second wastewater sample is collected from the same location as the first wastewater sample and is collected at least two days after collecting the first wastewater sample; detecting the concentration of at least one microorganism genus in the second wastewater sample; and identifying the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one microorganism genus is higher in the second wastewater sample than the first wastewater sample.

12. The method of claim 11, further comprising: detecting the concentration of at least one chemical metabolite in the first wastewater sample, wherein the at least one chemical metabolite is selected from the group consisting of: genistein, daidzein, enterolactone, and equol; and detecting the concentration of the at least one chemical metabolite in the second wastewater sample; wherein the population is identified as having the population as increased consumption of a plant-based diet upon detection that the concentration of the at least one chemical metabolite and the concentration of the at least one microorganism genus are higher in the second wastewater sample than the first wastewater sample.

13. The method of claim 12, wherein the at least one chemical metabolite enterolactone and equol.

14. The method of claim 13, wherein the concentration of the at least one chemical metabolite is detected using liquid chromatography (LC) with mass spectrometry (MS) or gas chromatography (GC) with mass spectrometry (MS).

15. The method of claim 13, wherein the concentration of the at least one chemical metabolite is detected using liquid chromatography with mass spectrometry.

16. The method of any one of claims 10-15, wherein the concentration of the at least one microorganism genus is detected by measuring the concentration of the 16S rRNA of the at least one microorganism genus.

17. The method of any one of claims 10-15, further comprising detecting the concentration of a second microorganism genus in the wastewater sample, wherein the second microorganism genus is selected from at least one member in the group consisting of: Slackia. Bacteroides, Eubacterium, Roseburia, Intestinibacter, Prevotella, and Senegalimassilia, whereby increased consumption of a plant-based diet in the population is confirmed upon detection that the concentration of the second microorganism genus is higher in the second wastewater sample than the first wastewater sample.

18. The method of claim 17, wherein the concentration of the at least one microorganism genus and the concentration of the second microorganism genus is detected by measuring the concentration of the 16S rRNA of the at least one microorganism genus and the second microorganism genus.

19. A kit for monitoring dietary behavior in a population, comprising: at least one agent capable of detecting the presence of at least one microorganism genus selected from the group consisting of Ruminococcus, Blaulia. Clostridium, Bifidobacterium, Romboutsia, Dorea, and Subdoligranulunr, and at least one an assay or chemical probe that detects at least one chemical metabolite selected from the group consisting of genistein, daidzein, enterolactone, and equol.

20. The kit of claim 19, further comprising:at least one agent that detects the presence of at least one microorganism genus selected from the group consisting of: Slackia. acler aides, Eubacterium, Roseburia, Iniesiini bacler, Prevotella, and Senegalimassilia.

21. The kit of claim 20, further comprising: at least one agent that detects the presence of at least one microorganism genus selected from the group consisting of: Faecalibacterium, Streptococcus, Akkermansia, and Lactobacillus.

22. The kit of any one of claims 19-21 comprising: at least one agent that detects the presence of Ruminococcus: at least one agent that detects the presence of Blautia,- at least one agent that detects the presence of Clostridium,- at least one agent that detects the presence of Bifidobacterium, - at least one agent that detects the presence of Romboutsia, at least one agent that detects the presence of Dorea, and at least one agent that detects the presence of Subdoligranulum.

23. The kit of any one of claims 19-21, wherein the agents that detect the presence of microorganisms are primers complementary to 16S rRNA of the microorganisms.

24. The kit of claim 22, wherein the agents that detect the presence of microorganisms are primers complementary to 16S rRNA of the microorganisms.

25. The kit of any one of claims 19-21, wherein the assay is a chromatographic assay selected from the group consisting of liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), and combinations thereof.

26. The kit of any one of claims 22, wherein the assay is a chromatographic assay selected from the group consisting of liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), and combinations thereof.

27. The kit of any one of claims 23, wherein the assay is a chromatographic assay selected from the group consisting of liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), and combinations thereof.

28. The kit of any one of claims 24, wherein the assay is a chromatographic assay selected from the group consisting of liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), and combinations thereof.

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