UBC Faculty Research and Publications

Ethnic and diet-related differences in the healthy infant microbiome Stearns, Jennifer C; Zulyniak, Michael A; de Souza, Russell J; Campbell, Natalie C; Fontes, Michelle; Shaikh, Mateen; Sears, Malcolm R; Becker, Allan B; Mandhane, Piushkumar J; Subbarao, Padmaja; Turvey, Stuart E; Gupta, Milan; Beyene, Joseph; Surette, Michael G; Anand, Sonia S Mar 29, 2017

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


52383-13073_2017_Article_421.pdf [ 961.73kB ]
JSON: 52383-1.0366940.json
JSON-LD: 52383-1.0366940-ld.json
RDF/XML (Pretty): 52383-1.0366940-rdf.xml
RDF/JSON: 52383-1.0366940-rdf.json
Turtle: 52383-1.0366940-turtle.txt
N-Triples: 52383-1.0366940-rdf-ntriples.txt
Original Record: 52383-1.0366940-source.json
Full Text

Full Text

RESEARCH Open AccessEthnic and diet-related differences in thehealthy infant microbiomeJennifer C. Stearns1,2*, Michael A. Zulyniak1, Russell J. de Souza3, Natalie C. Campbell1, Michelle Fontes1,2,Mateen Shaikh3, Malcolm R. Sears1, Allan B. Becker4, Piushkumar J. Mandhane5, Padmaja Subbarao6,Stuart E. Turvey7, Milan Gupta1, Joseph Beyene3, Michael G. Surette1,2 and Sonia S. Anand1,3,8 for the NutriGenAllianceAbstractBackground: The infant gut is rapidly colonized by microorganisms soon after birth, and the composition of themicrobiota is dynamic in the first year of life. Although a stable microbiome may not be established until 1 to3 years after birth, the infant gut microbiota appears to be an important predictor of health outcomes in later life.Methods: We obtained stool at one year of age from 173 white Caucasian and 182 South Asian infants from twoCanadian birth cohorts to gain insight into how maternal and early infancy exposures influence the developmentof the gut microbiota. We investigated whether the infant gut microbiota differed by ethnicity (referring to groupsof people who have certain racial, cultural, religious, or other traits in common) and by breastfeeding status, whileaccounting for variations in maternal and infant exposures (such as maternal antibiotic use, gestational diabetes,vegetarianism, infant milk diet, time of introduction of solid food, infant birth weight, and weight gain in thefirst year).Results: We demonstrate that ethnicity and infant feeding practices independently influence the infant gutmicrobiome at 1 year, and that ethnic differences can be mapped to alpha diversity as well as a higher abundance oflactic acid bacteria in South Asians and a higher abundance of genera within the order Clostridiales in white Caucasians.Conclusions: The infant gut microbiome is influenced by ethnicity and breastfeeding in the first year of life. Ethnicdifferences in the gut microbiome may reflect maternal/infant dietary differences and whether these differences areassociated with future cardiometabolic outcomes can only be determined after prospective follow-up.Keywords: Infant gut microbiome, Ethnicity, Breastfeeding, DietBackgroundThe developing gastrointestinal microbiota in the firstyears of life is important for immune function, nutrientmetabolism and protection from pathogens [1–3]. Mi-crobial colonization of the infant gut proceeds throughinfancy and establishment of an adult-like microbiome isestimated to occur within the first 3 years [4]. Identifyingfactors that shape the gut microbiome is currently an ac-tive area of research and early evidence suggests thathost genetics [5] and early life exposures, includingdelivery method, antibiotics [6, 7], and diet, influencethe infant gut microbiome [8, 9]. In addition to theseestablished roles, the gut microbiota is emerging as a po-tentially important contributor to the development ofnon-communicable diseases (NCDs), having been associ-ated with conditions such as obesity [10, 11], type 2 dia-betes [12, 13], allergy and atopy [14], inflammatorybowel disease [15], and the development of colon cancer[16]. The influence of the infant microbiome on the de-velopment of these conditions is of great clinical andeconomic interest as rates of NCDs in adults are in-creasing globally and by 2030 are predicted to accountfor 89% of all deaths in high income countries [17].South Asians are people whose ancestors originatefrom the Indian subcontinent and they have among the* Correspondence: stearns@mcmaster.ca1Department of Medicine, McMaster University, Hamilton, ON, Canada2Farncombe Family Digestive Health Research Institute, McMaster University,Hamilton, ON, CanadaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Stearns et al. Genome Medicine  (2017) 9:32 DOI 10.1186/s13073-017-0421-5highest rates of type 2 diabetes and premature cardio-vascular disease (CVD) in the world. CVD risk factors,including adiposity, type 2 diabetes, and dyslipidemia,are higher among South Asians compared to white Cau-casians of the same BMI [18]. There is preliminary evi-dence that gut microbial composition in adults andchildren varies by age [4, 19], dietary intake [20, 21], eth-nicity, geography [4, 22], and adoption of western life-styles [19, 23]. Bacterial richness has been shown toincrease with age and to be lower in residents of theUnited States compared with other populations [4]. Corebacterial metabolic genes varied between these popula-tions as well; however, the underlying reasons for ethnicand geographic differences in the microbiome have notbeen characterized. In this paper we investigate the asso-ciations of ethnicity and early life exposures with the gutmicrobiome among 1-year-old infants born and living inCanada while accounting for a diverse set of covariatesthat represent dietary differences as well as other expo-sures throughout infancy. This study explores the effectof ethnicity separately from region and provides a pre-liminary look at effects of ethnicity on the gut micro-biota in early life.MethodsCohortsParticipants from two prospective Canadian birth co-horts were included in this gut microbiome substudy.The Canadian Healthy Infant Longitudinal Developmentstudy (CHILD) enrolled 3624 mainly white Caucasianmother–child pairs and most fathers from four Canadiancenters (Vancouver, BC; Edmonton, AB; Winnipeg/Winkler-Morden, MB; and Toronto, ON) to investigatethe root causes of allergy and asthma, including geneticand environmental triggers, and the ways in which theyinteract [24–26]. In this analysis, ethnicity refers togroups of people who have certain racial, cultural, reli-gious, or other traits in common, whereas race refers toa person’s physical characteristics, such as bone struc-ture, or skin, hair, or eye color [27, 28]. In the CHILDcohort, white Caucasian ancestry was confirmed by par-ticipants’ response to the question “To which ethnic orcultural group did your parents belong?” The SouthAsian Birth Cohort (START-Canada) enrolled 1012South Asian mother–child pairs from the Brampton andPeel Region of Ontario to investigate the influence of di-verse environmental exposures and genetics on early lifeadiposity, growth trajectory, and cardiometabolic factors[29]. South Asian ethnicity was verified by the mother’sself-report of her and the father’s, and their parents’, an-cestral origin being from India, Pakistan, Sri Lanka, orBangladesh.Harmonization of clinical data across cohorts wasdone by extracting them with the same definitions,where possible. When questions were not identical, weworked to extract the data from each cohort in such away as to satisfy the same definition. Gestational dia-betes mellitus was defined as having diabetes on thebirth chart but no diabetes prior to pregnancy. A childwas considered to have had formula in the first year ifformula use was recorded at any time in the first year(from several questionnaires). In both cohorts, timing ofinfant weighing at 1 year was typically performed on thesame day as 1-year stool collection (r2 > 0.93; median =0 days; 95% confidence interval 0 to 2 days).In this gut microbiome substudy, 1-year fecal samplesfrom 173 white Caucasian infants in CHILD and 182South Asian infants in START were used for the mainanalysis. An additional 77 samples from the CHILD co-hort, from infants who are not white Caucasian, wereused to explore trends found in the main analysis. Forboth cohorts, the collection of 1-year fecal samples wasscheduled with the mother in advance. Stool collectionwas taken from a regular diaper in START and a spe-cially lined diaper in CHILD [24]. Mothers wereinstructed to record the time and date of the stool sam-ple and place it in a sterile bag in the refrigerator fortheir scheduled appointment with the research nurse.Upon arrival, the nurse used depyrogenized stainlesssteel spatulas to divide the sample between four pre-labeled cryovials. The cryovials were then transported tothe lab in a cooler, weighed, and stored at −80 °C or li-quid nitrogen. START samples were stored at 4 °C for2–4 h prior to freezing whereas CHILD samples werestored at 4 °C for an average of 14 ± 12 h.DNA extraction, 16S rRNA gene sequencing, and analysisDNA was extracted with a custom DNA extraction proto-col described in [30]. Briefly, 100–200 mg of stool wasadded to 2.8 mm and 0.1 mm glass beads (MoBio Labora-tories Inc., Carlsbad, CA, USA) along with 800 μl of200 mM sodium phosphate monobasic (pH 8) and 100 μlguanidinium thiocyanate EDTA N-lauroylsarkosine buffer(50.8 mM guanidine thiocyanate, 100 mM ethylenedi-aminetetraacetic acid, and 34 mM N-lauroylsarcosine).These were then homogenized in a PowerLyzer 24 BenchTop Homogenizer (MoBio Laboratories Inc.) for 3 min at3000 RPM. Next, two enzymatic lysis steps were per-formed. First, the sample was incubated with 50 μl of100 mg/ml lysozyme, 500 U mutanolysin, and 10 μl of10 mg/ml RNase for 1 h at 37 °C. Next, the sample was in-cubated with 25 μl 25% sodium dodecyl sulphate, 25 μl of20 mg/ml Proteinase K, and 62.5 μl of 5 M NaCl at 65 °Cfor 1 h. Next, debris was pelleted in a tabletop centrifugeat maximum speed for 5 min and the supernatant addedto 900 μl of phenol:chloroform:isoamyl alcohol (25:24:1).The sample was then vortexed and centrifuged at max-imum speed in a tabletop centrifuge for 10 min. TheStearns et al. Genome Medicine  (2017) 9:32 Page 2 of 12aqueous phase was removed and the sample run throughthe Clean and Concentrator-25 column (Zymo Research,Irvine, CA, USA) according to kit directions except forelution, which was done with 50 μl of ultrapure water andallowed to sit for 5 min before elution. The DNA wasquantified using a Nanodrop 2000c Spectrophotometer[30]. Amplification of the bacterial 16S rRNA gene v3 re-gion (150 bp) tags was performed as previously described[31] with the following changes: 5 pmol of primer,200 μM of each dNTP, 1.5 mM MgCl2, 2 μl of 10 mg/mlbovine serum albumin, and 1.25 U Taq polymerase (LifeTechnologies, Carlsbad, CA, USA) were used in a 50 μlreaction volume. The PCR program used was as follows:94 °C for 2 min followed by 30 cycles of 94 °C for 30 s,50 °C for 30 s, and 72 °C for 30 s, then a final extensionstep at 72 °C for 10 min. DNA extraction and PCR ampli-fication of 16S rRNA gene v3 libraries were found to bereproducible using a set of five samples from each cohort(total of ten samples) that were extracted in triplicate (29extractions since one extraction failed) and a subset ofthree extractions from each cohort amplified in triplicatefor a total of 41 datasets (Additional file 1: Figure S1).Illumina libraries were sequenced in the McMasterGenomics Facility with 250-bp sequencing in the for-ward and reverse directions on the Illumina MiSeq in-strument. Custom, in-house Perl scripts were used toprocess Illumina sequences as previously described [32].Briefly, after sequence trimming and alignment, oper-ational taxonomic units (OTU) were clustered usingAbundantOTU+ [33] with a threshold of 97%. Chimerachecking was not done since we have shown that ampli-fication of the short V3 region of the 16S rRNA geneleads to very few genuine chimeric sequences [34]. Tax-onomy for the representative sequence of each OTUwas assigned using the Ribosomal Database Project clas-sifier [35] with a minimum confidence cutoff of 0.8against the Greengenes (2013 release) reference database[36]. All OTUs classified as “Root:Other” (comprising0.03% of the total reads sequenced) were then excludedas was one sample with <500 sequenced reads; however,singleton OTUs were not excluded. This resulted in atotal of 41.4 million reads with a minimum of 2.0 × 103,maximum of 4.3 × 105, and a median of 9.0 × 104 readsper sample.Bacterial community richness and diversity (alphadiversity) were calculated using the estimated speciesrichness and Shannon diversity functions with thevegan package in R [37], using OTU abundances. Dif-ferences between bacterial communities in each sample(beta diversity) were quantified using the Bray–Curtisdissimilarity measure on relative abundance values ofall bacterial genera and principal coordinate analysiswas also done using the vegan package or the phyloseqpackage [38] in R.Statistical AnalysisSimple linear regression was used to determine the effectof ethnicity and breastfeeding on alpha diversity esti-mates. Permutational multivariate analysis of varianceon Bray–Curtis dissimilarities of genus level relativeabundances, done with the adonis function from thevegan package in R [37], was used to examine bacterialcommunity differences associated with ethnicity afteradjustment for potential covariates of ethnicity–micro-biome associations.Candidate covariates in the multivariable model wereinformed by the existing literature and assessed formallyin univariable models against microbiome diversity (i.e.,years mother lived in Canada, breastfeeding at time ofcollection, time since weaning, formula and cow’s milkuse in the first year, time of introduction of solid foods,infant weight gain in the first year, birth weight, infantage at stool collection, and mode of delivery, gestationaldiabetes, mother’s antibiotic use during pregnancy andlabor, and mother’s vegetarian status). Next, the candi-date variables chosen above were used to separately pre-dict dissimilarities with the same method as above.Those with p < 0.10 were subjected to a forward stepwiseprocedure. We then added the most significant covari-ates into the model in order of the proportion of vari-ance explained, and stopped when the next mostsignificant covariate was above the 0.05 threshold.The association between genus level abundances andethnicity and/or breastfeeding was determined through amultivariate algorithm adjusting for significant covariatesperformed with the Maaslin package in R [39, 40].Briefly, covariates found to be significant (p < 0.05) pre-dictors of the microbiome (described above) were in-cluded into a multivariate boosted, additive generallinear model between covariate data and bacterial genuslevel abundances. P values were adjusted for multipletesting with the false discovery rate, reported as q values,and q < 0.05 was considered significant. Genera with acoefficient of variation >0.001 were included in Add-itional file 2: Table S1.ResultsTable 1 shows the baseline demographic and anthropo-metric characteristics of the mothers and infants se-lected from CHILD (white Caucasians only) and START.Briefly, South Asian mothers lived in Canada for anaverage of 8 years versus a lifetime for white Caucasianmothers. Furthermore, South Asian mothers were youn-ger, more likely to be vegetarian (34% versus 2%, p <0.001), and to be diagnosed with gestational diabetesduring pregnancy (14% versus 4%, p < 0.001) comparedto white Caucasian mothers. There were no significantdifferences in the rates of Caesarian section between eth-nic groups (18% in South Asian versus 15% in whiteStearns et al. Genome Medicine  (2017) 9:32 Page 3 of 12Caucasian); however, white Caucasian mothers weremore likely to receive antibiotics during pregnancy (8%versus 0.5%, p < 0.001) and South Asian mothers weremore likely to receive antibiotics during labor (43% versus34%, p < 0.05). South Asian infants were born earlier (39.1weeks versus 39.5 weeks, p < 0.05), had lower birth weight(3.3 kg versus 3.5 kg, p < 0.001), and gained more weightin the first year of life (7.1 kg gained versus 6.4 kg gained,p < 0.001) than did white Caucasian infants. While bothwhite Caucasian and South Asian mothers reported thatthey breastfed their infants at some point during the firstyear (97.1% versus 94.4%), a greater proportion of SouthAsian infants were still breastfeeding at the time of 1-yearstool sample collection (43% versus 32%, p < 0.05).Additionally, there was more formula use during the firstyear (77% versus 65%, p < 0.001) and earlier introductionof solid food among South Asians (88% versus 50% from 3to 6 months, 9.4% versus 40% from 6 to 9 months, p <0.001). We suspect that more South Asian infant dietswere vegetarian on account of the greater proportion oftheir mothers who identified as vegetarian (34% versus2%, p < 0.001). Furthermore, there was no difference inage at time of stool collection (p = 0.39) between whiteCaucasians (12.3 ± 1.71 months) and South Asians (12.4 ±1.69 months; Table 1).Abundance of microorganisms within all samplesThe v3 region of 16S rRNA genes was profiled from 355participant stool samples collected at 1 year of age, 173white Caucasians from the CHILD cohort and 182 fromthe START cohort. The range of alpha diversity esti-mates for each ethnicity separated by current breastfeed-ing at the time of sampling is illustrated in Fig. 1. Usingsimple linear regression, species richness estimates werefound to be significantly affected by ethnicity after takinginto account breastfeeding at time of collection (p <0.05). Shannon diversity was significantly affected byTable 1 Mother and infant characteristicsWhite Caucasians South Asians P valueMaternal characteristicsaNumber per group 173 182Maternal age in years (SD) 31.9 (4.13) 30.5 (4.03) 0.002#Maternal height (cm) 166.3 (6.54)c 161.5 (6.54)b <0.001#Maternal pre-pregnancyBMI (if available)24.6 (4.65)c 24.2 (4.51) >0.05#Maternal weight gain 15.2 (5.73)f 15.3 (8.99)b >0.05#Years mother lived inCanada (SD)29.5 (7.71)b 7.7 (5.96) <0.001#Vegetarian status ofmother N (%)‡4 (2.31%) 62 (34.07%)b <0.001*Gestational diabetesN (%)7 (4.05%)b 26 (14.29%)b <0.001*Mode of delivery N (%)Vaginal 123 (82.00%)c 121 (78.57%) >0.05*C-Section 27 (18.00%)c 33 (21.43%)Antibiotics during pregnancyYes 14 (8.09%) 1 (0.55%) <0.001*No 159 (91.91%) 181 (99.45%)Antibiotics during laborYes 59 (38.06%)d 79 (45.40%)b 0.036*No 96 (61.94%)d 95 (54.60%)bInfant covariatesaCurrently breastfeedingat sample collection NYes 56 (36.13%)b 78 (43.33%)b 0.036*No 99 (63.87%)b 102 (56.67%)bBreastfed in the first yearYes 167 (97.1%)b 170 (94.4%)b >0.05*No 5 (2.9%)b 10 (5.5%)bTime since weaning inmonths (SD)5.6 (4.02)f 7.1 (5.85)f 0.045#Currently using cow’s milkYes 68 (57.63%)e 121 (67.98%)b 0.019*No 50 (42.37%)e 57 (32.02%)bCurrently using formulaYes 47 (31.13%)d 72 (39.56%) 0.026*No 104 (68.87%)d 110 (60.44%)Formula in the first yearYes 99 (63.06%)c 154 (90.06%)c <0.001*No 58 (36.94%)c 17 (9.94%)cTime in months ofintroduction of solids0–3 6 (4.03%)d 2 (1.11%)b <0.001§3–6 75 (50.34%)d 159 (88.33%)b6–9 66 (44.30%)d 17 (9.44%)b9–12 2 (1.34%)d 2 (1.11%)bTable 1 Mother and infant characteristics (Continued)Birth weight in kg (SD) 3.5 (0.47)c 3.3 (0.48) <0.001#Weight gain in the firstyear in kg (SD)6.4 (1.21)c 7.1 (1.31) <0.001#Age of infant at time ofsample collection inmonths (SD)12.3 (1.71) 12.4 (1.69) 0.39#Gestational age in weeks(SD)39.5 (1.33)b 39.1 (1.36) 0.006#awhere there is no superscript there was no missing databLess than 5.00% data missingcLess than 10.00% data missingd10–20% data missinge32% data missingf40–50% data missing*Fischer’s exact test§Cochrane Armitage trend test‡Maternal vegetarian status was used as a surrogate for infant diet exposure#T-testStearns et al. Genome Medicine  (2017) 9:32 Page 4 of 12ethnicity, taking into account breastfeeding at time ofcollection (p < 0.001), and likewise breastfeeding at timeof collection within each ethnicity significantly affectedShannon diversity (p < 0.05). Further, when START sam-ples, all collected within the Brampton/Peel region ofOntario Canada, were compared with each study centerwithin the CHILD cohort (Vancouver, Edmonton, Toronto,and Winnipeg/Winkler-Morden) only Winnipeg/Winkler-Morden, MB had significantly lower species richness esti-mates (p < 0.05; Additional file 1: Figure S2). Althoughthere was variability in Shannon diversity estimates acrosssample sites for CHILD, all sites were found to have signifi-cantly lower diversity than the START samples (p < 0.05;Additional file 1: Figure S2), while accounting for currentbreastfeeding. By including sample sites into the regressionmodel the effect of current breastfeeding on Shannon di-versity was no longer significant (p = 0.054).Differences in the relative abundance of the dominantbacterial genera are presented in Additional file 1: FigureS3, broken down by ethnic group and breastfeeding status.Heterogeneity of samples can be seen in Additional file 1:Figure S3 as well as differences in genus level microbobialprofiles between ethnic groups and breastfeeding status,differences that are explored in detail below.Principal coordinate analysis of Bray–Curtis dissimilar-ities illustrates between-community differences in sam-ples from white Caucasians and South Asian infants.Variation in the gut microbiome across geography hasbeen observed in studies involving adults [41]; however,in our study the effect of ethnicity was larger than theeffect of geographic location (Fig. 2) shown as the separ-ation of the centroid for samples from South Asiansfrom the centroids of samples from white Caucasiansfrom all study centers. Also evident from the principalcoordinate analysis, breastfeeding at time of collectionaffected the gut microbial profiles, although when strati-fied by currently breastfed and not currently breastfedinfants, the strong effect of ethnicity persisted (Fig. 2).Several studies have found the infant gut microbiome tovary between infants born by Caesarean section andthose born vaginally with the effect diminishing withage. Here, delivery method was not found to be a signifi-cant predictor of the structure of the gut microbiome in1-year-old infants (Additional file 1: Figure S4). This maybe because differences were no longer strong enough tobe detected or because members of the phylum Bacteroi-detes, often missing from the gut microbiome in Caesar-ean section delivered infants, were not abundant in ourvaginally born infants (Additional file 1: Figure S2).Association between ethnicity, milk diet, and solid fooddietIn addition to ethnicity, 13 potential covariates were alsoassociated with the microbiome in univariable regressionanalysis. These included mother’s years living in Canada,infant age, breastfeeding status at time of collection,time since weaning, vegetarian status, timing of intro-duction of solid foods, birth weight, infant weight gainin the first year, antibiotics during pregnancy, antibioticsduring labor, formula use in the first year, formula use atcollection, and cow’s milk in the first year (all p < 0.10;Table 2). We entered this set of covariates into a forwardstepwise regression model to determine which factorsremained significant and independently influenced thegut microbiome. Only ethnicity (p < 0.001), breastfeedingstatus (p < 0.001), infant age at stool collection (p < 0.01),and weight gain in the first year (p < 0.01) remained in-dependently associated with the gut microbiome as awhole.There was no statistically significant multiplicativeinteraction between ethnicity and breastfeeding (p =0.23). Nevertheless, we acknowledge that such tests maybe underpowered, and thus the results were also strati-fied by ethnicity and breastfeeding status in order toexamine trends. Forward stepwise regression was con-ducted within white Caucasians and separately withinSouth Asians (Table 3). This revealed that breastfeeding(p < 0.01) and infant age (p < 0.05) were independentlyassociated with differences in the microbiome withineach ethnic group, while antibiotic use during labor (p <0.05) and weight gain in the first year (p < 0.05)remained independently associated with differences inthe microbiome only in white Caucasians. Forward step-wise regression was also conducted separately within in-fants breastfed and not breastfed at the time ofcollection (Table 4), which indicated that ethnicity (p <0.01) and the infant age (p < 0.05) remained independ-ently associated with differences in the gut microbiomein both groups.Fig. 1 Alpha diversity measures within white Caucasians and SouthAsians, split by breastfeeding status at the time of sample collection.Whiskers extend to the most extreme data values up to 1.5× theinterquartile range; data outside this range are shown as circlesStearns et al. Genome Medicine  (2017) 9:32 Page 5 of 12Differentially abundant genera within each groupDifference in the relative abundance of the dominantbacterial genera is presented as a taxa bar chart in Add-itional file 1: Figure S2, broken down by ethnic groupand breastfeeding status. The relative abundance of indi-vidual bacterial genera was assessed for association withethnicity and breastfeeding while accounting for infantage and weight gain in the first year. These covariates,which had survived the stepwise regression on the entirecommunity, were included in the multivariate algorithmin order to strike a balance between overfitting themodel and identifying the most comprehensive list ofpredictors. Taxa significantly associated with ethnicity,breastfeeding at time of collection, infant weight gain inthe first year, and infant age (q value <0.05) are listed inAdditional file 2: Table S1 and their abundance is illus-trated in Fig. 3.South Asians had higher abundances of several generawithin the Actinobacteria (Bifidobacterium, Collinsella,Actinomyces, Atopobium) and of three unclassified gen-era compared to white Caucasians. Genera within thephylum Firmicutes within two distinct taxonomic groupswere associated with ethnicity. Genera such as Strepto-coccus, Enterococcus, and Lactobacillus (class Bacilli,order Lactobacillales) were more abundant within SouthAsians whereas genera such as Blautia, Pseudobutyrivi-brio, Ruminococcus, and Oscillospira (order Clostri-diales) were more abundant in white Caucasians. Themost differentially abundant genus were unclassifiedmembers of the Lachnospiraceae which were higher inwhite Caucasians. In order to investigate whether thesedifferences were specific to each cohort or were indica-tive of true ethnic differences, five genera significantlyassociated with either white Caucasians or South Asianswere plotted among the small number of South Asiansrecruited within the CHILD cohort (n = 6 that were notused for the previous microbiome analysis). Despite thesmall number available, the same trends were seen forthe five genera plotted (Additional file 1: Figure S5).Not surprisingly, breastfeeding status at the timeof sample collection was strongly associated with theabundance of the genera Bifdobacterium (phylumFig. 2 Principal coordinate analyses (PCoA) of Bray–Curtis dissimilarities. Centroids for ethnicity, breastfeeding status at time of collection, andstudy center are shown as circles with lines radiating to samplesStearns et al. Genome Medicine  (2017) 9:32 Page 6 of 12Actinobacteria; Fig. 3). Several genera within thephylum Firmicutes were associated with breastfeeding atthe time of collection; some were more abundant (Veillo-nella, Megasphaera, and Dialister) and others were lessabundant (Blautia, unclassified Lachospiraceae, Clostrid-ium, Ruminococcus, Coprobacillus, Lactococcus, as well asseveral unclassified genera within the Clostridiales andErysipelotrichales).DiscussionOur results demonstrate that the gut microbiome of in-fants is influenced by ethnicity, infant age, weight gain,and breastfeeding. The gut microbiome has been pro-posed to influence the progression of chronic diseasesand has been associated with adverse health outcomes[42]. Development of the microbiome within the firstyears of life may influence long-term health, and can beaffected by perinatal, genetic, and dietary factors, includ-ing solid foods and milk diet.The distribution of a number of maternal and infantparameters differed between white Caucasian and SouthAsians (i.e., vegetarian status, gestational diabetes melli-tus prevalence, timing of introduction of solid foods,antibiotic use during pregnancy, mode of delivery, etc.)and thus seemed likely candidates to explain the micro-biome differences by ethnicity. However, when these var-iables were added as independent predictors of the gutmicrobiome composition in the multivariable model,none except breastfeeding status at the time of sampling,infant age, and weight gain in the first year improved thefit of the model (ethnicity R2 = 0.084 versus R2 = 0.082with all additional variables; breastfeeding status R2 =0.040 versus R2 = 0.032 with all additional variables).This suggests that these variables were largely capturedby the higher order variables of interest, i.e., ethnicityand breastfeeding. Next, after taking into account thesesignificant predictors (breastfeeding status, infant age at1-year stool, and weight gain in the first year of life) wefound that groups of bacterial genera which are phylo-genetically distinct (i.e., within the order Lactobacillalesversus Clostridales) were present at different abundanceswithin each ethnic group. This suggests that differentmetabolic strategies are at work within the gut micro-biome of South Asian and white Caucasian infants. Add-itionally, these bacterial taxa are good candidates topredict diet-related influences on the microbiome, mi-crobial influences on host metabolism, and bacterialstimulation of the host immune system [43].Several members of the lactic acid bacteria (LAB), spe-cifically Bifidobacterium, Lactococcus, Streptococcus, andEnterococcus, were more abundant within South Asiansafter taking into account breastfeeding status at the timeof collection, infant age, and weight gain in the first year.LAB break down mainly carbohydrates that are notabsorbed by the host to produce acetate and lactate,both of which are used as energy sources by other mi-crobial groups [43, 44]. Also the abundance of membersof the Atopobium cluster of Actinobacteria (i.e., generaTable 2 Univariable and multivariable permutational analysis of variance using Bray–Curtis dissimilarity matricesPredictor variable Univariable MultivariableF-Model R2 Pr(>F) F-Model R2 Pr(>F)Ethnicity 32.61 0.084 0.001 32.21 0.084 0.001Years in Canada 24.57 0.066 0.001 NAaInfant age at time of sample collection 3.79 0.011 0.003 3.75 0.01 0.002Breastfeeding at 1 year 14.28 0.040 0.001 12.75 0.033 0.001Time since weaning 5.46 0.018 0.001 NADelivery mode 0.82 0.005 0.64 NAVegetarian status 10.45 0.029 0.001 NAGestational diabetes 1.49 0.004 0.16 NAAntibiotics during pregnancy 2.07 0.006 0.04 NAAntibiotics during labor 2.56 0.008 0.02 NATime of introduction of solid food 1.80 0.016 0.02 NABirth weight 3.09 0.009 0.006 NAInfant weight gain in the first year 5.09 0.014 0.001 3.08 0.008 0.01Formula at collection 1.05 0.004 0.36 NAFormula in the first year 2.35 0.007 0.03 NACow’s milk in the first year 1.85 0.006 0.06 NAMultivariable model chosen by forward stepwise regressionaNot included as highly collinear with ethnicityNA not applicableStearns et al. Genome Medicine  (2017) 9:32 Page 7 of 12such as Collinsella and Atopobium) was higher in SouthAsians. This group of bacteria are saccharolytic (i.e., theybreak down small sugars) [45] and have been seen to de-crease in abundance in the microbiome of individualswith a diet rich in whole grains [46]. These genera havealso been associated with higher levels of low-densitylipoprotein in humans [47] and, along with other mem-bers of the Actinobacteria, have been associated withhigh hepatic levels of triglycerides and low hepatic levelsof glycogen and glucose in mice [48]. It is of interest tonote that these observations are based on v3 16S rRNAgene data. Several studies of the infant gut microbiome,which employ amplification and sequencing of othervariable regions of the same gene often report very lowlevels of Actinobacteria [6, 9, 49]. Members of thisphylum, such as the Bifidobacteria, have been shown todominate the infant gut microbiome [4, 50, 51], suggest-ing a possible primer bias against this group.In contrast, white Caucasians showed higher abundancesof members of the Firmicutes from the order Clostridiales,which have been shown to be increased in response to di-ets rich in animal protein [52] and high in fat [53]. Prod-ucts of bacterial fermentation of acetate and lactate,mentioned above, as well as non-digestible fiber andTable 3 Subgroup analysis based on ethnicity. Permutational analysis of variance using Bray-Curtis dissimilarity matricesPredictor variable Univariable MultivariableF-Model R2 Pr(>F) F-Model R2 Pr(>F)White CaucasiansBreastfeeding 6.52 0.038 0.001 4.18 0.03 0.001Time since weaning 2.81 0.020 0.007 NAAge at time of sample collection 2.89 0.017 0.007 2.5 0.015 0.02Delivery mode 0.98 0.012 0.48 NAAntibiotics during pregnancy 0.52 0.003 0.86 NAAntibiotics during labor 2.45 0.016 0.03 NAVegetarian status 2.33 0.013 0.02 NAGestational diabetes 0.85 0.005 0.52 NATime of introduction of solid food 1.12 0.023 0.32 NAFormula at collection 1.23 0.009 0.26 NAFormula in the first year 2.51 0.016 0.01 NACow’s milk in the first year 1.29 0.01 0.23 NAWeight gain in the first year 2.23 0.014 0.04 2.8 0.017 0.01Birth weight 0.35 0.002 0.95 NASouth AsiansBreastfeeding 8.79 0.047 0.001 8.88 0.047 0.001Age at time of collection 3.75 0.020 0.003 2.84 0.015 0.008Time since weaning 5.23 0.030 0.001 NADelivery mode 1.10 0.012 0.34 NAAntibiotics during pregnancy 0.52 0.003 0.85 NAAntibiotics during labor 2.45 0.016 0.02 NAVegetarian status 1.32 0.007 0.22 NAGestational diabetes 0.48 0.003 0.88 NAYears lived in Canada 0.92 0.005 0.46 NATime of introduction of solid food 1.35 0.022 0.14 NAFormula at collection 2.07 0.015 0.05 NAFormula in the first year 1.95 0.01 0.06 NACow’s milk in the first year 2.24 0.012 0.04 NAWeight gain in the first year 1.40 0.008 0.19 NABirth weight 1.27 0.007 0.24 NAPermutational analysis of variance using Bray-Curtis dissimilarity matricesNA not applicableStearns et al. Genome Medicine  (2017) 9:32 Page 8 of 12oligosaccharides by members of the Clostridiales seen here(Ruminococcus, Lachnospiraceae, and Oscillospira) includeshort chain fatty acids like butyrate, which is used by hostcells as an energy source and can signal increased barrierfunction [43]. Though also proposed to be chemoprotec-tive, the relationship between luminal butyrate exposureand colorectal cancer in humans has been examined onlyindirectly in case-control studies [54]. Nevertheless, thesefindings suggest different metabolic processes and immunestimuli at work within the South Asian and white Cauca-sian infant gastrointestinal tract, some of which may be ex-plained by their heterogeneous diets.When switching from a milk-based diet to a solidfood diet, prior studies have shown a decrease in theabundance of Bifidobacterium along with an increasein members of the Firmicutes (such as Clostridiumsp.) and Bacteroidetes [12, 20]. One study suggeststhat it is the cessation of breastfeeding that is re-quired for maturation of the gut microbiota to occurwith a decrease in Bifidobacterium and an increase inmembers of the Clostridiales only occurring afterweaning [9]. As expected, after adjustment for ethni-city, infant age, and weight gain in the first year, Bifi-dobacterium and Lactobacillus were significantlyTable 4 Subgroup analysis of breastfed and not currently breastfed children at time of collection. Permutational analysis of varianceusing Bray-Curtis dissimilarity matricesF-Model R2 Pr(>F) F-Model R2 Pr(>F)Currently breastfedEthnicity 15.43 0.104 0.001 18.01 0.112 0.001Age 1.78 0.014 0.13 2.43 0.015 0.02Delivery mode 1.06 0.015 0.38 NAAntibiotics during pregnancy 1.32 0.009 0.21 NAAntibiotics during labor 3.16 0.023 0.009 NAVegetarian status 5.69 0.040 0.001 2.17 0.01 0.04Gestational diabetes 1.05 0.007 0.36 NAYears lived in Canada 10.39 0.070 0.001 NATime of introduction of solid food 4.88 0.066 0.001 NAFormula at collection 0.72 0.007 0.65 NAFormula in the first year 2.81 0.02 0.009 NACow’s milk in the first year 0.94 0.008 0.43 NAWeight gain in the first year 7.26 0.05 0.001 3.55 0.02 0.008Birth weight 2.74 0.019 0.01 NANot currently breastfedEthnicity 16.10 0.074 0.001 11.54 0.06 0.001Infant age 2.46 0.012 0.02 2.14 0.01 0.03Time since weaning 3.20 0.019 0.002 NADelivery mode 1.03 0.010 0.40 NAAntibiotics during pregnancy 2.10 0.010 0.049 NAAntibiotics during labor 1.54 0.008 0.15 NAVegetarian status 3.76 0.019 0.002 NAGestational diabetes 1.27 0.006 0.22 NAYears in Canada 12.79 0.062 0.001 NATime of introduction of solid food 2.25 0.035 0.006 NAFormula at collection 1.63 0.009 0.11 NAFormula in the first year 2.48 0.013 0.01 NACow’s milk in the first year 0.88 0.005 0.55 NAWeight gain in the first year 2.25 0.01 0.03 NABirth weight 2.20 0.011 0.03 NANA not applicableStearns et al. Genome Medicine  (2017) 9:32 Page 9 of 12associated with breastfeeding. Additionally, an in-crease in the abundance of several genera within thephylum Firmicutes were associated with not beingbreastfed at the time of sampling.Bifidobacteria, along with the LAB, are known to beabundant members of the microbiome of breastfeedinginfants [55], whereas genera within the order Clostri-diales are known to be more abundant within the gut ofadults [56]. Here bacterial profiles indicative of a breastmilk diet were common among South Asians even thosethat were not breastfeeding at the time of collection,suggesting that these infants retain more of a breastfeed-ing microbiome than do white Caucasians of the sameage. The reasons for this are unclear; however, dietarydifferences may be contributing. Our data show thatequal proportions of infants in both groups were breast-fed in the first year but does not capture breastfeedingfrequency. It also shows that there was a much higherrate of formula use and an earlier introduction to solidfood within South Asian than within white Caucasian in-fants. Because self-reported vegetarianism was more fre-quent in South Asians, it is possible that meatconsumption hastens, or non-meat diets delay, changesinduced within the infant gut microbiome during theswitch to a solid food diet. It is important to note, how-ever, that to our knowledge an analysis of the adultSouth Asian microbiome has not been reported, nor hasa description of the maturation of the South Asian infantmicrobiome toward an adult-like composition; thus, ourdata must be interpreted within the context of the study,i.e., of South Asian infants born in Canada who consumea South Asian diet.The underlying construct of “Ethnicity” brings togetherseveral biological and cultural factors, and it can be charac-terized using a number of different parameters (e.g., dietaryhabits, ancestral country of origin, etc.). In our multivariatemodel, ethnicity and breast feeding status remained inde-pendent and significant predictors of differences in theoverall microbial communities (beta diversity), whereasvegetarian diet did not, which implies that the impact ofethnicity which incorporates some unique dietary patternsis not wholly explained by these dietary differences, as italso reflects other differences between the groups. Afterensuring that these additional factors were potentiallyaccounted for (i.e., years living in Canada, antibiotic use,timing of solid food introduction, etc.) we observed thatbreastfeeding, infant age, and weight gain in the first yearsignificantly influenced the infant gut microbiome.Strengths of our study include its relatively large size ofnearly 200 infants from each of two different ethnic groupswho have diverse dietary intakes; the availability of stoolsamples collected at similar times using similar methods;the high quality deep sequencing of the 16S rRNA gene forbacterial identification; a reliability analysis to demonstratereproducibility of our methods; and detailed measurementof maternal and infant covariates. Limitations include in-complete data on maternal weight gain during and prior topregnancy, which limits our ability to assess the influenceof this important covariate on the infant gut microbiome;ethnicity in this study refers to the group a person self-identifies with and reflects a mix of cultural factors,including language, diet, religion, and ancestry—thus,ethnicity is a multidimensional construct which includessome within-group heterogeneity, and differences attribut-able to ethnicity may reflect a broad range of factors whichare not purely biological; and the lack of a direct measureof infant dietary intake beyond feeding type at the time ofstool collection.Fig. 3 Genera differentially associated with ethnicity (white Caucasian(WC) and South Asian (SA)), breastfeeding (breastfeeding (BF) and notbreastfeeding (nBF)), infant age, or infant weight gain in the firstyear (wt gain), through the multivariate boosted additive model toolMaaslin. Bacterial relative abundance means across each categoryshown as the size and significance as the shade of each circle (darker =smaller p value; Additional file 2: Table S1). Significant association ofthe microbiome with the continuous variables weight gain or age isshown with symbols (positively (+) or negatively (−) associated;Additional file 2: Table S1). Genera sorted taxonomically withsubgroups within the Firmicutes labeled in greyStearns et al. Genome Medicine  (2017) 9:32 Page 10 of 12ConclusionsThe infant gut microbiome is influenced by ethnicityand breastfeeding in the first year of life. Ethnic differ-ences in the gut microbiome may reflect maternal/infantdietary differences and whether these differences are as-sociated with future cardiometabolic outcomes can onlybe determined after prospective follow-up.Additional filesAdditional file 1: Supplementary Figures S1–S5. (PDF 2180 kb)Additional file 2: Supplementary Table S1. (DOCX 151 kb)AbbreviationsCHILD: Canadian Healthy Infant Longitudinal Development study;CVD: Cardiovascular disease; LAB: Lactic acid bacteria; NCD: Non-communicabledisease; OTU: Operational taxonomic unit; START: South Asian Birth CohortAcknowledgementsWe are grateful to all the families who took part in this study, and the wholeSTART and CHILD teams, which includes interviewers, nurses, computer andlaboratory technicians, clerical workers, research scientists, volunteers, managers,and receptionists. In particular, we would like to thank Dipika Desai, NoraAbdalla, and Diana LeFebrevre for help with coordination of the studies as wellsas Laura Rossi for her contributions to the technical aspects of this work.FundingThis work was funded by a grant from the Canadian Institutes of HealthResearch (grant number FH6 129924). The CHILD Study was primarily fundedby CIHR and the Allergy, Genes and Environment (AllerGen) Network of Centresof Excellence. The START study was part of a bilateral ICMR/CIHR fundedprogram (grant number INC-109205) and from the Heart and Stroke Foundation(grant number NA7283). JCS holds the Endowed Farncombe Family Chair in Mi-crobial Ecology and Bioinformatics at McMaster University. SSA holds a CanadaResearch Chair in Ethnicity and Cardiovascular Disease and the Michael G.DeGroote Heart and Stroke Foundation of Canada Chair in Population Health.MRS holds the AstraZeneca Endowed Chair in Respiratory Epidemiology. MGSholds a Canada Research Chair in Interdisciplinary Microbiome Research. MAZholds a CIHR RCT Fellowship grant (MTP201410, MAZ).Availability of data and materialsThe datasets generated and/or analyzed during the current study are notpublicly available, since the CHILD and START studies are bound by consentand cannot provide identifiable information to an outside group, but areavailable from the corresponding author on reasonable request.Authors’ contributionsJCS analyzed and interpreted the microbiome data. NCC, MAZ, RDS, SSA, andJB harmonized and assisted in the analysis of the subject data across cohortsand MF processed stool samples and contributed to technical aspects of themicrobiome profiles within the lab of MGS. MG and SSA coordinated thecollection of samples and data for START. MRS, ABB, PJS, PM, and SETcoordinated the collection of samples and data for CHILD. JCS, MAZ, RDS,MS, and SSA were major contributors in writing the manuscript. All authorsread and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicableEthics approval and consent to participateThe Hamilton Integrated Research Ethics Board (HIREB) approved theresearch protocols for studies on human samples (CHILD, Malcolm Sears REBProject #07-2929; START, Sonia Anand HIREB Project # 10-640) and eachparticipating parent gave signed informed consent. Our study conforms tothe Declaration of Helsinki.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Department of Medicine, McMaster University, Hamilton, ON, Canada.2Farncombe Family Digestive Health Research Institute, McMaster University,Hamilton, ON, Canada. 3Department of Health Research Methods, Evidence,and Impact, McMaster University, Hamilton, ON, Canada. 4Department ofImmunology, Faculty of Medicine, University of Manitoba, Winnipeg,Manitoba, Canada. 5Department of Pediatrics, Faculty of Medicine andDentistry, University of Alberta, Edmonton, Alberta, Canada. 6Hospital for SickChildren & Department of Paediatrics, University of Toronto, Toronto, ON,Canada. 7BC Children’s Hospital and Child and Family Research Institute,Department of Paediatrics, Faculty of Medicine, University of BritishColumbia, Vancouver, British Columbia, Canada. 8Population Health ResearchInstitute, Hamilton Health Sciences and McMaster University, Hamilton,Ontario, Canada.Received: 22 June 2016 Accepted: 4 March 2017References1. Falk PG, Hooper LV, Midtvedt T, Gordon JI. Creating and maintaining thegastrointestinal ecosystem: what we know and need to know fromgnotobiology. Microbiol Mol Biol Rev. 1998;62:1157–70.2. Guarner F, Malagelada J-R. Gut flora in health and disease. Lancet. 2003;361:512–9.3. Newburg DS, Walker WA. Protection of the neonate by the innate immunesystem of developing gut and of human milk. Pediatr Res. 2007;61:2–8.4. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, ContrerasM, et al. Human gut microbiome viewed across age and geography. Nature.2012;486:222–7.5. Li Y, Oosting M, Deelen P, Ricaño-Ponce I, Smeekens S, Jaeger M, et al.Inter-individual variability and genetic influences on cytokine responses tobacteria and fungi. Nat Med. 2016;22:952–60.6. Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M, Li H, et al.Antibiotics, birth mode, and diet shape microbiome maturation during earlylife. Sci Transl Med. 2016;8:343ra82.7. Azad MB, Konya T, Maughan H, Guttman DS, Field CJ, Chari RS, et al. Gutmicrobiota of healthy Canadian infants: profiles by mode of delivery andinfant diet at 4 months. CMAJ. 2013;185:385–94.8. Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, et al.Human genetics shape the gut microbiome. Cell. 2014;159:789–99.9. Bäckhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, et al.Dynamics and stabilization of the human gut microbiome during the firstyear of life. Cell Host Microbe. 2015;17:852.10. Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI.Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102:11070–5.11. Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS. A taxonomic signature ofobesity in the microbiome? Getting to the guts of the matter. PLoS One.2014;9:e84689.12. Laursen MF, Andersen LBB, Michaelsen KF, Mølgaard C, Trolle E, Bahl MI,et al. Infant gut microbiota development is driven by transition to familyfoods independent of maternal obesity. mSphere. 2016;1:e00069–15.13. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wideassociation study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60.14. Penders J, Gerhold K, Stobberingh EE, Thijs C, Zimmermann K, Lau S, et al.Establishment of the intestinal microbiota and its role for atopic dermatitisin early childhood. J Allergy Clin Immunol. 2013;132:601–607.e8.15. Knights D, Lassen KG, Xavier RJ. Advances in inflammatory bowel diseasepathogenesis: linking host genetics and the microbiome. Gut. 2013;62:1505–10.16. Irrazábal T, Belcheva A, Girardin SE, Martin A, Philpott DJ. The multifacetedrole of the intestinal microbiota in colon cancer. Mol Cell. 2014;54:309–20.Stearns et al. Genome Medicine  (2017) 9:32 Page 11 of 1217. Nikolic IA, Stanciole AE, Zaydman M. Chronic emergency: why NCDs matter.Health, Nutrition, and Population Discussion Paper. 2011. http://www.ghd-net.org/sites/default/files/ChronicEmergencyWhyNCDsMatter.pdf. AccessedJune 2016.18. Rana A, de Souza RJ, Kandasamy S, Lear SA, Anand SS. Cardiovascular riskamong South Asians living in Canada: a systematic review and meta-analysis. CMAJ Open. 2014;2:E183–91.19. Clemente JC, Pehrsson EC, Blaser MJ, Sandhu K, Gao Z, Wang B, et al. Themicrobiome of uncontacted Amerindians. Sci Adv. 2015;1. doi:10.1126/sciadv.150018320. Fallani M, Amarri S, Uusijarvi A, Adam R, Khanna S, Aguilera M, et al.Determinants of the human infant intestinal microbiota after theintroduction of first complementary foods in infant samples from fiveEuropean centres. Microbiology. 2011;157:1385–92.21. Zhang J, Guo Z, Xue Z, Sun Z, Zhang M, Wang L, et al. A phylo-functionalcore of gut microbiota in healthy young Chinese cohorts across lifestyles,geography and ethnicities. ISME J. 2015;9:1979–90.22. Martínez I, Stegen JC, Maldonado-Gómez MX, Eren AM, Siba PM, GreenhillAR, et al. The gut microbiota of rural papua new guineans: composition,diversity patterns, and ecological processes. Cell Rep. 2015;11:527–38.23. Schnorr SL. The diverse microbiome of the hunter-gatherer. Nature. 2015;518:S14–5.24. Moraes TJ, Lefebvre DL, Chooniedass R, Becker AB, Brook JR, Denburg J,et al. The Canadian healthy infant longitudinal development birth cohortstudy: biological samples and biobanking. Paediatr Perinat Epidemiol. 2015;29:84–92.25. Takaro TK, Scott JA, Allen RW, Anand SS, Becker AB, Befus AD, et al. TheCanadian Healthy Infant Longitudinal Development (CHILD) birth cohortstudy: assessment of environmental exposures. J Expo Sci EnvironEpidemiol. 2015;25:580–92.26. Subbarao P, Anand SS, Becker AB, Befus AD, Brauer M, Brook JR, et al. TheCanadian Healthy Infant Longitudinal Development (CHILD) Study: examiningdevelopmental origins of allergy and asthma. Thorax. 2015;70:998–1000.27. Anand SS. Using ethnicity as a classification variable in health research:perpetuating the myth of biological determinism, serving socio-politicalagendas, or making valuable contributions to medical sciences? EthnHealth. 1999;4:241–4.28. de Souza RJ, Anand SS. Cardiovascular disease in Asian Americans:unmasking heterogeneity. J Am Coll Cardiol. 2014;64:2495–7.29. Anand SS, Vasudevan A, Gupta M, Morrison K, Kurpad A, Teo KK, et al.Rationale and design of South Asian birth cohort (START): a Canada-Indiacollaborative study. BMC Public Health. 2013;13:79.30. Stearns JC, Davidson CJ, McKeon S, Whelan FJ, Fontes ME, Schryvers AB,et al. Culture and molecular-based profiles show shifts in bacterialcommunities of the upper respiratory tract that occur with age. ISMEJ. 2015;9:1246–59.31. Bartram AK, Lynch MDJ, Stearns JC, Moreno-Hagelsieb G, Neufeld JD.Generation of multimillion-sequence 16S rRNA gene libraries from complexmicrobial communities by assembling paired-end illumina reads. ApplEnviron Microbiol. 2011;77:3846–52.32. Whelan FJ, Verschoor CP, Stearns JC, Rossi L, Johnstone J, Surette MG, et al.The loss of topography in the microbial communities of the upperrespiratory tract in the elderly. Ann Am Thorac Soc. 2014;11:513–21.33. Ye Y. Identification and quantification of abundant species frompyrosequences of 16S rRNA by consensus alignment. 2010 IEEEInternational Conference on Bioinformatics and Biomedicine (BIBM). NewYork: Institute of Electrical and Electronics Engineers (IEEE); 2010. pp. 153–7.doi:10.1109/BIBM.2010.5706555.34. Stearns JC, Lynch MDL, Senadheera DB, Tenenbaum HC, Goldberg MB,Cvitkovitch DG, et al. Bacterial biogeography of the human digestive tract.Sci Rep. 2011;1:1–9.35. Wang Q, George MG, Tiedje JM, Cole JR. Naive Bayesian classifier for rapidassignment of rRNA sequences into the new bacterial taxonomy. ApplEnviron Microbiol. 2007;73:5261–7.36. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al.Greengenes, a chimera-checked 16S rRNA gene database and workbenchcompatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.37. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, et al.vegan: Community Ecology Package. 2015. http://CRAN.R-project.org/package = vegan.38. McMurdie PJ, Holmes S. Phyloseq: An R Package for reproducibleinteractive analysis and graphics of microbiome census data. PLoS One.2013;8:e61217.39. Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, et al.Dysfunction of the intestinal microbiome in inflammatory bowel diseaseand treatment. Genome Biol. 2012;13:R79.40. Tickle T, Huttenhower C. Maaslin. Multivariate statistical framework that findsassociations between clinical metadata and microbial communityabundance or function. 2014. https://bitbucket.org/biobakery/maaslin41. Dugas LR, Fuller M, Gilbert J, Layden BT. The obese gut microbiome acrossthe epidemiologic transition. Emerg Themes Epidemiol. 2016;13:2.42. Aron-Wisnewsky J, Clément K. The gut microbiome, diet, and links tocardiometabolic and chronic disorders. Nat Rev Nephrol. 2016;12:169–81.43. Flint HJ, Duncan SH, Scott KP, Louis P. Links between diet, gut microbiotacomposition and gut metabolism. Proc Nutr Soc. 2015;74:13–22.44. Duncan SH, Louis P, Flint HJ. Lactate-utilizing bacteria, isolated from humanfeces, that produce butyrate as a major fermentation product. Appl EnvironMicrobiol. 2004;70:5810–7.45. Thorasin T, Hoyles L, McCartney AL. Dynamics and diversity of the “Atopobiumcluster” in the human faecal microbiota, and phenotypic characterization of“Atopobium cluster” isolates. Microbiology. 2015;161:565–79.46. Martínez I, Lattimer JM, Hubach KL, Case JA, Yang J, Weber CG, et al. Gutmicrobiome composition is linked to whole grain-induced immunologicalimprovements. ISME J. 2013;7:269–80.47. Lahti L, Salonen A, Kekkonen RA, Salojärvi J, Jalanka-Tuovinen J, Palva A,et al. Associations between the human intestinal microbiota, Lactobacillusrhamnosus GG and serum lipids indicated by integrated analysis of high-throughput profiling data. PeerJ. 2013;1:e32.48. Claus SP, Ellero SL, Berger B, Krause L, Bruttin A, Molina J, et al. Colonization-induced host-gut microbial metabolic interaction. MBio. 2011;2:e00271–10.49. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of thehuman infant intestinal microbiota. PLoS Biol. 2007;5:e177.50. Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. Factorsinfluencing the composition of the intestinal microbiota in early infancy.Pediatrics. 2006;118:511–21.51. Turroni F, Peano C, Pass DA, Foroni E, Severgnini M, Claesson MJ, et al.Diversity of bifidobacteria within the infant gut microbiota. PLoS One. 2012;7:e36957.52. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y-Y, Keilbaugh SA, et al.Linking long-term dietary patterns with gut microbial enterotypes. Science.2011;334:105–8.53. de La Serre CB, Ellis CL, Lee J, Hartman AL, Rutledge JC, Raybould HE.Propensity to high-fat diet-induced obesity in rats is associated withchanges in the gut microbiota and gut inflammation. Am J PhysiolGastrointest Liver Physiol. 2010;299:G440–8.54. Sengupta S, Muir JG, Gibson PR. Does butyrate protect from colorectalcancer? J Gastroenterol Hepatol. 2006;21:209–18.55. Solís G, de Los Reyes-Gavilan CG, Fernández N, Margolles A, Gueimonde M.Establishment and development of lactic acid bacteria and bifidobacteriamicrobiota in breast-milk and the infant gut. Anaerobe. 2010;16:307–10.56. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity,stability and resilience of the human gut microbiota. Nature. 2012;489:220–30.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Stearns et al. Genome Medicine  (2017) 9:32 Page 12 of 12


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items