FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2953115794> ?p ?o ?g. }

• W2953115794 endingPage "802.e5" @default.
• W2953115794 startingPage "789" @default.
• W2953115794 abstract "•Daily microbiome variation is related to food choices, but not to conventional nutrients•Daily microbiome variation depends on at least two days of dietary history•Similar foods have different effects on different people’s microbiomes Diet is a key determinant of human gut microbiome variation. However, the fine-scale relationships between daily food choices and human gut microbiome composition remain unexplored. Here, we used multivariate methods to integrate 24-h food records and fecal shotgun metagenomes from 34 healthy human subjects collected daily over 17 days. Microbiome composition depended on multiple days of dietary history and was more strongly associated with food choices than with conventional nutrient profiles, and daily microbial responses to diet were highly personalized. Data from two subjects consuming only meal replacement beverages suggest that a monotonous diet does not induce microbiome stability in humans, and instead, overall dietary diversity associates with microbiome stability. Our work provides key methodological insights for future diet-microbiome studies and suggests that food-based interventions seeking to modulate the gut microbiota may need to be tailored to the individual microbiome. Trial Registration: ClinicalTrials.gov: NCT03610477. Diet is a key determinant of human gut microbiome variation. However, the fine-scale relationships between daily food choices and human gut microbiome composition remain unexplored. Here, we used multivariate methods to integrate 24-h food records and fecal shotgun metagenomes from 34 healthy human subjects collected daily over 17 days. Microbiome composition depended on multiple days of dietary history and was more strongly associated with food choices than with conventional nutrient profiles, and daily microbial responses to diet were highly personalized. Data from two subjects consuming only meal replacement beverages suggest that a monotonous diet does not induce microbiome stability in humans, and instead, overall dietary diversity associates with microbiome stability. Our work provides key methodological insights for future diet-microbiome studies and suggests that food-based interventions seeking to modulate the gut microbiota may need to be tailored to the individual microbiome. Trial Registration: ClinicalTrials.gov: NCT03610477. The microbial ecosystem within the human gastrointestinal tract is dynamic and complex, and its composition is known to vary widely across healthy individuals (Human Microbiome Project Consortium, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6999) Google Scholar). When measured within the same individual over a longitudinal period, large shifts in microbial composition can take place in response to disease or environmental changes (David et al., 2014aDavid L.A. Materna A.C. Friedman J. Campos-Baptista M.I. Blackburn M.C. Perrotta A. Erdman S.E. Alm E.J. Host lifestyle affects human microbiota on daily timescales.Genome Biol. 2014; 15: R89Crossref PubMed Scopus (542) Google Scholar, Flores et al., 2014Flores G.E. Caporaso J.G. Henley J.B. Rideout J.R. Domogala D. Chase J. Leff J.W. Vázquez-Baeza Y. Gonzalez A. Knight R. et al.Temporal variability is a personalized feature of the human microbiome.Genome Biol. 2014; 15: 531Crossref PubMed Scopus (259) Google Scholar, Fukuyama et al., 2017Fukuyama J. Rumker L. Sankaran K. Jeganathan P. Dethlefsen L. Relman D.A. Holmes S.P. Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment.PLoS Comput. Biol. 2017; 13: e1005706Crossref PubMed Scopus (44) Google Scholar). Substantial changes in microbiome composition have been measured in response to changes in dietary intake, such as those seen with a shift from plant-based to animal-based diets (David et al., 2014bDavid L.A. Maurice C.F. Carmody R.N. Gootenberg D.B. Button J.E. Wolfe B.E. Ling A.V. Devlin A.S. Varma Y. Fischbach M.A. et al.Diet rapidly and reproducibly alters the human gut microbiome.Nature. 2014; 505: 559-563Crossref PubMed Scopus (5616) Google Scholar) or those seen after the addition of individual nutrients (Maier et al., 2017Maier T.V. Lucio M. Lee L.H. VerBerkmoes N.C. Brislawn C.J. Bernhardt J. Lamendella R. McDermott J.E. Bergeron N. Heinzmann S.S. et al.Impact of dietary resistant starch on the human gut microbiome, metaproteome, and metabolome.MBio. 2017; 8Crossref PubMed Scopus (175) Google Scholar). However, controlled feeding trials have revealed that inter-subject microbiome variation remains high even after periods of identical dietary intake (Wu et al., 2011Wu G.D. Chen J. Hoffmann C. Bittinger K. Chen Y.Y. Keilbaugh S.A. Bewtra M. Knights D. Walters W.A. Knight R. et al.Linking long-term dietary patterns with gut microbial enterotypes.Science. 2011; 334: 105-108Crossref PubMed Scopus (4127) Google Scholar). In population-level studies, diet consistently accounts for only a small proportion of microbiome variation (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (1220) Google Scholar, Rothschild et al., 2018Rothschild D. Weissbrod O. Barkan E. Kurilshikov A. Korem T. Zeevi D. Costea P.I. Godneva A. Kalka I.N. Bar N. et al.Environment dominates over host genetics in shaping human gut microbiota.Nature. 2018; 555: 210-215Crossref PubMed Scopus (1316) Google Scholar, Vangay et al., 2018Vangay P. Johnson A.J. Ward T.L. Al-Ghalith G.A. Shields-Cutler R.R. Hillmann B.M. Lucas S.K. Beura L.K. Thompson E.A. Till L.M. et al.US immigration westernizes the human gut microbiome.Cell. 2018; 175: 962-972Abstract Full Text Full Text PDF PubMed Scopus (345) Google Scholar), and only modest differences have been found between groups of people consuming vastly different dietary patterns, such as omnivores and vegetarians (Wu et al., 2016Wu G.D. Compher C. Chen E.Z. Smith S.A. Shah R.D. Bittinger K. Chehoud C. Albenberg L.G. Nessel L. Gilroy E. et al.Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production.Gut. 2016; 65: 63-72Crossref PubMed Scopus (318) Google Scholar). Recently, microbiome composition has been found to predict biomarkers of blood glucose control (Korem et al., 2017Korem T. Zeevi D. Zmora N. Weissbrod O. Bar N. Lotan-Pompan M. Avnit-Sagi T. Kosower N. Malka G. Rein M. et al.Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses.Cell Metab. 2017; 25: 1243-1253Abstract Full Text Full Text PDF PubMed Scopus (164) Google Scholar, Zeevi et al., 2015Zeevi D. Korem T. Zmora N. Israeli D. Rothschild D. Weinberger A. Ben-Yacov O. Lador D. Avnit-Sagi T. Lotan-Pompan M. et al.Personalized nutrition by prediction of glycemic responses.Cell. 2015; 163: 1079-1094Abstract Full Text Full Text PDF PubMed Scopus (1357) Google Scholar) and weight loss (Thaiss et al., 2016Thaiss C.A. Itav S. Rothschild D. Meijer M. Levy M. Moresi C. Dohnalová L. Braverman S. Rozin S. Malitsky S. et al.Persistent microbiome alterations modulate the rate of post-dieting weight regain.Nature. 2016; Crossref Scopus (292) Google Scholar) in a personalized way. While it is understood that diet broadly affects microbiome composition within an individual (Gentile and Weir, 2018Gentile C.L. Weir T.L. The gut microbiota at the intersection of diet and human health.Science. 2018; 362: 776-780Crossref PubMed Scopus (496) Google Scholar), an understanding of the exact importance of specific foods and nutrients in shaping microbiome composition across populations remains elusive. The lack of robust methods to assess and pair complex food intake from free-living populations with existing microbiome analysis pipelines is a limitation of current efforts to understand how dietary intake affects microbiome composition. Studies assessing direct diet-microbiome relationships have largely relied on food frequency questionnaires (FFQs) and conventional nutrient profiles from macro- and micronutrients (Wu et al., 2011Wu G.D. Chen J. Hoffmann C. Bittinger K. Chen Y.Y. Keilbaugh S.A. Bewtra M. Knights D. Walters W.A. Knight R. et al.Linking long-term dietary patterns with gut microbial enterotypes.Science. 2011; 334: 105-108Crossref PubMed Scopus (4127) Google Scholar, Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (1020) Google Scholar). Studies of broad diet-microbiome relationships have used principal-component analysis (Strate et al., 2017Strate L.L. Keeley B.R. Cao Y. Wu K. Giovannucci E.L. Chan A.T. Western dietary pattern increases, and prudent dietary pattern decreases, risk of incident diverticulitis in a prospective cohort study.Gastroenterology. 2017; 152: 1023-1030Abstract Full Text Full Text PDF PubMed Scopus (90) Google Scholar) and correspondence analysis (Claesson et al., 2012Claesson M.J. Jeffery I.B. Conde S. Power S.E. O’Connor E.M. Cusack S. Harris H.M.B. Coakley M. Lakshminarayanan B. O’Sullivan O. et al.Gut microbiota composition correlates with diet and health in the elderly.Nature. 2012; 488: 178-184Crossref PubMed Scopus (2106) Google Scholar) to define dietary patterns (i.e., “prudent” versus “western”) or dietary indices based on a priori knowledge (i.e., the healthy eating index [HEI]) (Bowyer et al., 2018Bowyer R.C.E. Jackson M.A. Pallister T. Skinner J. Spector T.D. Welch A.A. Steves C.J. Use of dietary indices to control for diet in human gut microbiota studies.Microbiome. 2018; 6: 77Crossref PubMed Scopus (68) Google Scholar). While dietary patterns can be linked to differences in microbiome composition across individuals (Claesson et al., 2012Claesson M.J. Jeffery I.B. Conde S. Power S.E. O’Connor E.M. Cusack S. Harris H.M.B. Coakley M. Lakshminarayanan B. O’Sullivan O. et al.Gut microbiota composition correlates with diet and health in the elderly.Nature. 2012; 488: 178-184Crossref PubMed Scopus (2106) Google Scholar, Wu et al., 2011Wu G.D. Chen J. Hoffmann C. Bittinger K. Chen Y.Y. Keilbaugh S.A. Bewtra M. Knights D. Walters W.A. Knight R. et al.Linking long-term dietary patterns with gut microbial enterotypes.Science. 2011; 334: 105-108Crossref PubMed Scopus (4127) Google Scholar), longitudinal pairing of diet and microbiome data is needed to assess how temporal variation in diet alters short-term microbiome stability, composition, and function. Similarly, diet assessment methods that rely solely on conventional nutrient profiles (i.e., macro- and micronutrients from nutrient databases) from 24-h dietary recalls or self-reported food records overlook information about foods that could have important microbial-dependent influences on health (Barratt et al., 2017Barratt M.J. Lebrilla C. Shapiro H.Y. Gordon J.I. The gut microbiota, food science, and human nutrition: a timely marriage.Cell Host Microbe. 2017; 22: 134-141Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar), such as the presence of phenolic compounds (Thaiss et al., 2016Thaiss C.A. Itav S. Rothschild D. Meijer M. Levy M. Moresi C. Dohnalová L. Braverman S. Rozin S. Malitsky S. et al.Persistent microbiome alterations modulate the rate of post-dieting weight regain.Nature. 2016; Crossref Scopus (292) Google Scholar), unique conformations of polysaccharides or microbiota-accessible carbohydrates (MACs) (Smits et al., 2016Smits S.A. Marcobal A. Higginbottom S. Sonnenburg J.L. Kashyap P.C. Individualized responses of gut microbiota to dietary intervention modeled in humanized mice.mSystems. 2016; 1Crossref PubMed Scopus (34) Google Scholar, Sonnenburg et al., 2016Sonnenburg E.D. Smits S.A. Tikhonov M. Higginbottom S.K. Wingreen N.S. Sonnenburg J.L. Diet-induced extinctions in the gut microbiota compound over generations.Nature. 2016; 529: 212-215Crossref PubMed Scopus (957) Google Scholar), and plant-derived exosomal microRNAs that persist through the gut and impact microbial metabolism and composition (Teng et al., 2018Teng Y. Ren Y. Sayed M. Hu X. Lei C. Kumar A. Hutchins E. Mu J. Deng Z. Luo C. et al.Plant-derived exosomal microRNAs shape the gut microbiota.Cell Host Microbe. 2018; 24: 637-652Abstract Full Text Full Text PDF PubMed Scopus (320) Google Scholar). To investigate the fine-scale relationships between daily food choices and human gut microbiome composition, we conducted an ultra-dense longitudinal study of the impact of habitual diet on the microbiome, including daily shotgun fecal metagenomic microbiome sequencing for 17 days from 34 subjects, paired with complete daily 24-h dietary records. We developed and applied multivariate methods for modeling dietary intake that move beyond conventional nutrient-based analysis. Combining daily shotgun metagenomics with daily diet provided a uniquely rich dataset for measuring the effects of diet on the personalization of microbial dynamics. Our dense longitudinal dataset also allowed us to investigate relationships between dietary intake and temporal microbial stability (Zaneveld et al., 2017Zaneveld J.R. McMinds R. Vega Thurber R. Stress and stability: applying the Anna Karenina principle to animal microbiomes.Nat. Microbiol. 2017; 2: 17121Crossref PubMed Scopus (371) Google Scholar). Our study was designed to identify the relationship between habitual dietary intake and daily microbiome variation using dense, longitudinal diet-microbiome data. To characterize the longitudinal relationship between diet and microbiome composition, we collected dietary intake data and fecal samples from 34 subjects for 17 consecutive days (see Table 1 for cohort characteristics by gender and STAR Methods for detailed subject information). Female subjects had significantly lower weight, height, waist circumference, and high-density lipoprotein than did male subjects (Student's t test, p value < 0.05). Daily food records were collected using the automated self-administered 24-h (ASA24) dietary assessment tool (2016, National Cancer Institute, Bethesda, MD, USA) (Park et al., 2018Park Y. Dodd K.W. Kipnis V. Thompson F.E. Potischman N. Schoeller D.A. Baer D.J. Midthune D. Troiano R.P. Bowles H. et al.Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.Am. J. Clin. Nutr. 2018; 107: 80-93Crossref PubMed Scopus (166) Google Scholar). Interestingly, the reported diet of two subjects (11 and 12) consisted primarily of a nutritional meal replacement beverage in 4 different flavors (Soylent, Rosa Foods). We refer to these subjects as “shake drinkers.” The study also included a 10-day, parallel, double-blind intervention trial to test the impact of medium chain triglycerides (MCTs) compared to long chain dietary triglycerides from extra virgin olive oil (EVOO) on microbiome composition (STAR Methods; see Table S1 for characteristics of study subjects by dietary supplementation arm). As there were only null findings from all tests for associations between MCT or EVOO supplementation and the microbiome (Figure S1), this manuscript focuses on the analysis of overall diet-microbiome covariation using all available samples.Table 1Subject Characteristics by GenderCharacteristicOverallFemale (n = 20)Male (n = 14)p value (female versus male)Age (years)31 ± 1029 ± 1034 ± 110.2Weight (kg)69 ± 1560 ± 881 ± 13<0.001Height (cm)172 ± 9165 ± 5180 ± 8<0.001Waist circumference (cm)87 ± 883 ± 691 ± 80.006Cholesterol (mg/dL)164 ± 32169 ± 34158 ± 300.6Trigs (mg/dL)66 ± 2067 ± 1965 ± 220.1HDL (mg/dL)57 ± 1159 ± 1255 ± 90.02LDL (mg/dL)94 ± 2997 ± 3190 ± 280.9Glucose (mg/dL)86 ± 785 ± 887 ± 60.4Insulin (mU/L)8 ± 48 ± 29 ± 40.5Values shown as mean ± standard deviation. p values calculated using Student’s t test. Abbreviations: Trigs, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein. Open table in a new tab Values shown as mean ± standard deviation. p values calculated using Student’s t test. Abbreviations: Trigs, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein. We conducted shotgun metagenomic sequencing on each stool sample at a depth of 7,195,302 ± 2,442,901 single-end reads per subject, divided approximately evenly across the time points for each subject, with an average depth of 506,133 ± 323,896 reads per sample after the removal of human DNA. We have shown previously that low-depth metagenomic sequencing can recover species-level taxonomic assignments and also allows for the assessment of functional profiles (Hillmann et al., 2018Hillmann B. Al-Ghalith G.A. Shields-Cutler R.R. Zhu Q. Gohl D.M. Beckman K.B. Knight R. Knights D. Evaluating the information content of shallow shotgun metagenomics.mSystems. 2018; 3Crossref Scopus (183) Google Scholar). We removed sequencing adaptors, trimmed and filtered shotgun metagenomic reads according to quality using SHI7 (Al-Ghalith et al., 2018Al-Ghalith G.A. Hillmann B. Ang K. Shields-Cutler R. Knights D. SHI7 is a self-learning pipeline for multipurpose short-read DNA quality control.mSystems. 2018; 3Crossref Scopus (48) Google Scholar), and assigned taxonomy using BURST (Al-Ghalith and Knights, 2017Al-Ghalith G. Knights D. BURST enables optimal exhaustive DNA alignment for big data.2017Google Scholar) and a database consisting of all bacterial strains annotated at chromosome-level assembly or better in RefSeq version 86. We retained 483 microbiome samples for analysis after removal of those with low depth. Dietary outliers were identified according to guidelines from ASA24 by comparing macronutrient composition and total energy intake to reference levels to identify low quality reporting. We retained 566 24-h food records after removal of dietary outliers (see Figure S1 for data availability by study day). While the shake drinkers were outliers in terms of the number and types of foods consumed, they were not outliers in terms of nutritional composition (Figure 1D). These subjects were retained in the dataset for a unique subset analysis of dietary stability but were excluded from analyses of habitual dietary intake and microbiome composition. Microbiome composition was more variable in some subjects than others across the study period (Figure 1A). As has previously been shown (Human Microbiome Project Consortium, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6999) Google Scholar), the most prevalent microbial functional modules, as annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000Kanehisa M. Goto S. KEGG: Kyoto encyclopedia of genes and genomes.Nucleic Acids Res. 2000; 28: 27-30Crossref PubMed Scopus (18179) Google Scholar), were highly consistent within and across subjects (Figure S2); however, we found a subset of functions to be highly variable (high coefficient of variation) across all samples (Figure 1B). The three most variable functions were the BaeS-BaeR (envelope stress response) two-component regulatory system module (M00450), the denitrification module (M00529) responsible for the conversion of nitrate to nitrogen, and the methanogenesis module (M00347) responsible for the conversion of formate to methane. When comparing average functional modules with average nutrient and food intake, we found no significant correlations between the most highly variable functional modules and dietary features in our samples. We hypothesize that these metagenomic functions may be highly variable because of personalized induction of different bacteria in response to dietary compounds that are not included in our nutritional database. Alternatively, functional variation may be due to stochastic fluctuations in bacterial abundance, syntrophic, or predator-prey relationships within the microbiota that are difficult to predict. While dietary intake (in terms of food choices) was highly personalized and variable between and within subjects (Figure 1C), macro- and micronutrient profiles were relatively stable across the study period (Figure 1D). Therefore, to incorporate the maximum amount of observed dietary complexity into our analysis, we chose to study diet in terms of reported food choices. While a small number of food choices were shared by more than 20 subjects (i.e., banana, coffee, cheddar cheese, lettuce, carrots, chicken breast), many foods were consumed by only one subject over the 17-day study period (Figure 2A). This is to be expected with dietary data because, like microbiome data, food intake data are often zero-inflated with highly correlated features (Zhang et al., 2011Zhang S. Midthune D. Guenther P.M. Krebs-Smith S.M. Kipnis V. Dodd K.W. Buckman D.W. Tooze J.A. Freedman L. Carroll R.J. A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment.Ann. Appl. Stat. 2011; 5: 1456-1487Crossref PubMed Scopus (75) Google Scholar), meaning that people episodically eat unique foods and they often consume meals within conventional food pairings. To account for the highly individualized nature of food choices, we borrowed from concepts previously developed for ecological analysis of the microbiome (Lozupone and Knight, 2005Lozupone C. Knight R. UniFrac: a new phylogenetic method for comparing microbial communities.Appl. Environ. Microbiol. 2005; 71: 8228-8235Crossref PubMed Scopus (5342) Google Scholar). We created a phenetic, hierarchical tree of foods from the Food and Nutrient Database for Dietary Studies (FNDDS) (U.SDepartment of AgricultureAgricultural Research Service, 2014U.S. Department of Agriculture, Agricultural Research Service USDA food and nutrient database for dietary studies.2014: 2011-2012Google Scholar) (Figure 2B; STAR Methods), which allowed us to apply the tree-based beta-diversity metric, UniFrac (Lozupone and Knight, 2005Lozupone C. Knight R. UniFrac: a new phylogenetic method for comparing microbial communities.Appl. Environ. Microbiol. 2005; 71: 8228-8235Crossref PubMed Scopus (5342) Google Scholar), and the tree-based alpha-diversity metric, Faith’s phylogenetic diversity (Faith, 1992Faith D.P. Conservation evaluation and phylogenetic diversity.Biol. Conserv. 1992; 61: 1-10Crossref Scopus (3221) Google Scholar), to dietary data. This approach shares statistical information across related foods, enabling us to measure the tree-based similarity between each pair of diet profiles, the overall tree-based diversity of total foods consumed by a person, and the tree-based diversity of food sources for a particular nutrient such as fiber. Microbiome beta-diversity analysis using Aitchison’s distances showed strong grouping by subject (Figure 2C; PERMANOVA; p value = 0.001; 999 permutations) (Aitchison et al., 2000Aitchison J. Barceló-Vidal C. Martín-Fernández J.A. Pawlowsky-Glahn V. Logratio analysis and compositional distance.Math. Geol. 2000; 32: 271-275Crossref Scopus (283) Google Scholar). Aitchison’s distance was selected for the microbiome beta-diversity metric to account for compositionality of relative abundance profiles. Average microbiome beta-diversity (Aitchison’s distances) did not show grouping by gender (PERMANOVA; p value = 0.2; 999 permutations). Using QIIME 1.9.1 (Caporaso et al., 2010Caporaso J.G. Kuczynski J. Stombaugh J. Bittinger K. Bushman F.D. Costello E.K. Fierer N. Peña A.G. Goodrich J.K. Gordon J.I. et al.QIIME allows analysis of high-throughput community sequencing data.Nat. Methods. 2010; 7: 335-336Crossref PubMed Scopus (24741) Google Scholar) and our hierarchical tree of foods, we calculated unweighted UniFrac beta-diversity of food profiles. Food beta-diversity also showed significant within-subject grouping (Figure 2D; PERMANOVA; p value = 0.001; 999 permutations). This within-subject grouping is most obvious for subject 8 (lower right quadrant) and subject 28 (lower left quadrant) relative to the rest of the study cohort. Average food beta-diversity (unweighted UniFrac) did show grouping by gender (PERMANOVA; p value = 0.002; 999 permutations). We applied Procrustes analysis to test for microbiome variation and dietary variation across subjects. Our analysis showed that a subject’s average food intake corresponds with that subject’s average microbiome composition when analyzed using the unweighted UniFrac-based food distances (Figure 2E; Procrustes; Monte Carlo p value = 0.008) and Aitchison’s microbiome distances. This finding persisted when we used an exploratory approach to apply UniFrac to low-depth shotgun metagenomics (Silverman et al., 2017Silverman J.D. Washburne A.D. Mukherjee S. David L.A. A phylogenetic transform enhances analysis of compositional microbiota data.Elife. 2017; 6Crossref Scopus (144) Google Scholar) (STAR Methods; Figures S3C and S3D). Interestingly, we did not find a similar correspondence between average microbiome and average diet when using non-tree-based food distances calculated directly from the food choice profiles (Figure S3E; Procrustes of microbiome Aitchison and standardized food profile Euclidean distances; Monte Carlo p value = 0.6), nor from average nutrient intake using 65 macro- and micronutrients (Figure 2F; Procrustes of microbiome Aitchison and standardized nutrient profile Euclidean distances; Monte Carlo p value = 0.4), further reinforcing the utility of applying tree-based metrics to food-intake profiles instead of relying on conventional nutrient profiles to understand dietary relationships with the microbiome. Average nutrient intake did correspond with tree-based food distances (Figure S3F; Procrustes; Monte Carlo p value = 0.002), demonstrating that the tree-based food distances do capture information about nutrient composition. Using constrained ordination with the first 5 principal coordinates in unweighted Unifrac tree-based food distance space, we found that diet accounted for 44% of the total variation in average microbiome composition, although food distances between people are confounded with differences in gender, BMI, and age, which independently accounted for 34% of the unconstrained explained variation in community structure. In addition to analyzing overall between-subject dietary variation, we used the tree-based diet method to compare between-subject variation in fiber sources with the microbiome. We calculated fiber-source beta-diversity using unweighted UniFrac distances for four separate food groups that have high fiber content: grains, fruits, vegetables, and legumes. Using these distances, we compared fiber-source beta-diversity to microbiome beta-diversity and found that subjects who obtained their fruit fiber or their grain fiber from similar foods tended to have more similar microbiome profiles (Figures 2G and 2H; Procrustes; Monte Carlo p value = 0.036 and 0.032, respectively). Controlled feeding trials assessing the specific impact of fiber-source variety on microbiome composition are needed to investigate these relationships further, particularly to investigate the impact of vegetable and legume fiber (Figures S3G and S3H, respectively). We hypothesized that microbiome composition and food choices would pair longitudinally within each subject. We assessed the pairing of microbiome beta-diversity and tree-based food beta-diversity longitudinally within each subject using Procrustes analysis. We completed this analysis for 29 subjects consuming a habitual diet for whom we had at least 10 longitudinal time points available. For 78% of our subjects, we found significant longitudinal pairing of diet with the microbiome when using a decaying weighted average of dietary history weighted by 2−n for the nth prior day of food records to pair with a given microbiome sample (Monte Carlo p value ≤ 0.05; Figure 3A; STAR Methods). Interestingly, only 21% of our subjects had significant pairing using the single-day preceding food record for each microbiome sample, but pairing efficacy improved by adding additional days of food records. When considering median p values across the subjects, the decaying average described above performed optimally; using the sum of 2, 3, 4, or 5 days of food records performed almost as well (Figure 3B) and resulted in significant pairing for most of the same subjects as captured by the decaying weighted average (Figure 3C). To test whether this improved multi-day food-record-microbiome association was simply due to the gastrointestinal transit time causing a delayed effect of single-day diet on the microbiome, versus a more complex interaction between multiple days of diet and varying growth rates among the microbial taxa, we compared single-day diet-microbiome associations when using day −1 (1 day prior) or day −2, −3, −4, or −5 as the single day. We found that longitudinal Procrustes pairing of the microbiome composition with a single day of dietary intake from between 1 and 5 days prior did not perform as well as using multiple days of dietary intake data for each microbiome sample (Figure S4). Combined, these findings suggest that daily microbiome variation depends on multiple days of recent dietary history. Daily longitudinal sampling allowed us to test directly for associations between dietary components and microbiome composition within each subject over time. We used the decaying weighted average of dietary intake (described above) prior to a given stool sample, and we collapsed dietary food choices into FNDDS fo" @default.
• W2953115794 created "2019-06-27" @default.
• W2953115794 creator A5011473019 @default.
• W2953115794 creator A5023686871 @default.
• W2953115794 creator A5029940723 @default.
• W2953115794 creator A5033049894 @default.
• W2953115794 creator A5044801664 @default.
• W2953115794 creator A5045991055 @default.
• W2953115794 creator A5047530325 @default.
• W2953115794 creator A5056704417 @default.
• W2953115794 creator A5058766687 @default.
• W2953115794 creator A5060648419 @default.
• W2953115794 creator A5063135788 @default.
• W2953115794 creator A5075346374 @default.
• W2953115794 creator A5081505232 @default.
• W2953115794 creator A5082358375 @default.
• W2953115794 date "2019-06-01" @default.
• W2953115794 modified "2023-10-14" @default.
• W2953115794 title "Daily Sampling Reveals Personalized Diet-Microbiome Associations in Humans" @default.
• W2953115794 cites W1811186957 @default.
• W2953115794 cites W1883997870 @default.
• W2953115794 cites W1950724050 @default.
• W2953115794 cites W1968105193 @default.
• W2953115794 cites W2006197413 @default.
• W2953115794 cites W2055055556 @default.
• W2953115794 cites W2061853512 @default.
• W2953115794 cites W2072970694 @default.
• W2953115794 cites W2088170171 @default.
• W2953115794 cites W2103088074 @default.
• W2953115794 cites W2105772474 @default.
• W2953115794 cites W2128769815 @default.
• W2953115794 cites W2136332263 @default.
• W2953115794 cites W2153755764 @default.
• W2953115794 cites W2159675211 @default.
• W2953115794 cites W2166562121 @default.
• W2953115794 cites W2166655850 @default.
• W2953115794 cites W2167123129 @default.
• W2953115794 cites W2170024099 @default.
• W2953115794 cites W2173732482 @default.
• W2953115794 cites W2175465469 @default.
• W2953115794 cites W2295084775 @default.
• W2953115794 cites W2326641739 @default.
• W2953115794 cites W2342540743 @default.
• W2953115794 cites W2344543878 @default.
• W2953115794 cites W2484230073 @default.
• W2953115794 cites W2510675082 @default.
• W2953115794 cites W2518217889 @default.
• W2953115794 cites W2527237309 @default.
• W2953115794 cites W2531570974 @default.
• W2953115794 cites W2542839199 @default.
• W2953115794 cites W2557198675 @default.
• W2953115794 cites W2569046310 @default.
• W2953115794 cites W2621835653 @default.
• W2953115794 cites W2742167857 @default.
• W2953115794 cites W2747111693 @default.
• W2953115794 cites W2749612575 @default.
• W2953115794 cites W2765206455 @default.
• W2953115794 cites W2779327387 @default.
• W2953115794 cites W2787902098 @default.
• W2953115794 cites W2792911384 @default.
• W2953115794 cites W2800530395 @default.
• W2953115794 cites W2802332168 @default.
• W2953115794 cites W2889977344 @default.
• W2953115794 cites W2898380126 @default.
• W2953115794 cites W2899495835 @default.
• W2953115794 cites W2900697674 @default.
• W2953115794 cites W2951076599 @default.
• W2953115794 cites W4294216483 @default.
• W2953115794 doi "https://doi.org/10.1016/j.chom.2019.05.005" @default.
• W2953115794 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31194939" @default.
• W2953115794 hasPublicationYear "2019" @default.
• W2953115794 type Work @default.
• W2953115794 sameAs 2953115794 @default.
• W2953115794 citedByCount "401" @default.
• W2953115794 countsByYear W29531157942018 @default.
• W2953115794 countsByYear W29531157942019 @default.
• W2953115794 countsByYear W29531157942020 @default.
• W2953115794 countsByYear W29531157942021 @default.
• W2953115794 countsByYear W29531157942022 @default.
• W2953115794 countsByYear W29531157942023 @default.
• W2953115794 crossrefType "journal-article" @default.
• W2953115794 hasAuthorship W2953115794A5011473019 @default.
• W2953115794 hasAuthorship W2953115794A5023686871 @default.
• W2953115794 hasAuthorship W2953115794A5029940723 @default.
• W2953115794 hasAuthorship W2953115794A5033049894 @default.
• W2953115794 hasAuthorship W2953115794A5044801664 @default.
• W2953115794 hasAuthorship W2953115794A5045991055 @default.
• W2953115794 hasAuthorship W2953115794A5047530325 @default.
• W2953115794 hasAuthorship W2953115794A5056704417 @default.
• W2953115794 hasAuthorship W2953115794A5058766687 @default.
• W2953115794 hasAuthorship W2953115794A5060648419 @default.
• W2953115794 hasAuthorship W2953115794A5063135788 @default.
• W2953115794 hasAuthorship W2953115794A5075346374 @default.
• W2953115794 hasAuthorship W2953115794A5081505232 @default.
• W2953115794 hasAuthorship W2953115794A5082358375 @default.
• W2953115794 hasBestOaLocation W29531157941 @default.
• W2953115794 hasConcept C104317684 @default.
• W2953115794 hasConcept C106131492 @default.

• next