Maternal Diet Shapes the Breast Milk Microbiota Composition and Diversity: Impact of Mode of Delivery and Antibiotic Exposure

Erika Cortes-Macías, Marta Selma-Royo, Izaskun García-Mantrana, Marta Calatayud, Sonia González, Cecilia Martínez-Costa, Maria Carmen Collado, Erika Cortes-Macías, Marta Selma-Royo, Izaskun García-Mantrana, Marta Calatayud, Sonia González, Cecilia Martínez-Costa, Maria Carmen Collado

Abstract

Background: Breast milk is a complex biofluid that provides nutrients and bioactive agents, including bacteria, for the development of the infant gut microbiota. However, the impact of maternal diet and other factors, such as mode of delivery and antibiotic exposure, on the breast milk microbiota has yet to be understood.

Objectives: This study aimed to examine the association between maternal diet and breast milk microbiota and to ascertain the potential role of mode of delivery and antibiotic exposure.

Methods: In a cross-sectional study of the MAMI cohort, breast milk microbiota profiling was assessed in 120 samples from healthy mothers by 16S rRNA gene sequencing. Maternal dietary information was recorded through an FFQ, and clinical characteristics, including mode of delivery, antibiotic exposure, and exclusive breastfeeding, were collected.

Results: Maternal diet was grouped into 2 clusters: Cluster I (high intake of plant protein, fiber, and carbohydrates), and Cluster II (high intake of animal protein and lipids). Breast milk microbiota was shaped by maternal dietary clusters. Staphylococcus and Bifidobacterium were associated with carbohydrate intake whereas the Streptococcus genus was associated with intakes of the n-3 PUFAs [EPA and docosapentaenoic acid (22:5ω-3)]. Mode of delivery and antibiotic exposure influenced breast milk microbiota in a diet cluster-dependent manner. Differences between/among the maternal dietary clusters were found in the milk microbiota of the cesarean-section (C-section)/antibiotic group, whereas no differences were observed in vaginal births. Lower abundances of Lactobacillus, Bacteroides, and Sediminibacterium genera were observed in Cluster II/C-section/antibiotic exposure compared with the other groups.

Conclusions: Maternal diet shapes the composition and diversity of breast milk microbiota, with the most important contributions coming from dietary fiber and both plant and animal protein intakes. The relation between the maternal diet and the milk microbiota needs further research because it has a key impact on infant microbiota development and contributes to infant health outcomes in the short and long term.This trial was registered at clinicaltrials.gov as NCT03552939.

Keywords: animal protein; breast milk; maternal diet; microbiota; plant protein.

© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society for Nutrition.

Figures

FIGURE 1
FIGURE 1
Maternal diet clusters and representative dietary components. The partitioning around medoid method showed that women were grouped (A) into 2 clusters and both distinct clusters (Cluster I = green, n = 44; and Cluster II = red, n = 76) are represented in the Principal Coordinate Analysis (PCoA) (B). The strongest contributors to the clustering were higher intake of animal protein and lower plant protein and dietary fiber. Diet was standardized by total energy intake to 2500 kcal/d (C–F). Middle line represents the media of all values and also, the upper and lower lines represent the standard deviation (SD)..
FIGURE 2
FIGURE 2
Breast milk microbiota is shaped by maternal diet clusters. Maternal factors contributing to milk microbiota variation (PERMANOVA Bray–Curtis model with 5 variables, P = 0.051) (n = 115) (A). The relative abundance of bacteria present in breast milk at phylum (B) and genus level (C) on maternal diet cluster, Cluster I (n = 42) and Cluster II (n = 73). Multivariate redundancy discriminant analysis (RDA) showed significant differences in breast milk microbial communities depending on maternal diet clusters, Cluster I (n = 42) and Cluster II (n = 73) (D). Microbial α diversity indexes according to maternal diet cluster, Cluster I (n = 42) and Cluster II (n = 73): richness (Chao1 index, E) and diversity (Shannon index, F). Richness and diversity values were adjusted for total bacterial load to normalize the values and avoid the impact of bacterial levels. Linear discriminant analysis (LDA) effect size (LEfSe) plot of taxonomic biomarkers was identified in both clusters: Cluster I (n = 42) (green) and Cluster II (n = 73) (red) (G). Specific associations between total bacterial load estimated by qPCR and α-diversity indexes, Chao 1 (H) and Shannon index (I) (n = 115). AB_birth, intrapartum antibiotic exposure; AB_gestation, antibiotic treatment during gestation; PC, Principal component; PERMANOVA, Permutational multivariate analysis of variance.
FIGURE 3
FIGURE 3
Correlations between breast milk microbial top 20 genera and the main dietary nutrients. Heatmaps of Spearman rank correlations between specific dietary nutrient intakes and the top 20 breast milk bacterial genus abundances (n = 115). Significant correlations (P < 0.05) are marked by an asterisk (*). Blue squares represent negative correlations, whereas red squares show positive correlations.
FIGURE 4
FIGURE 4
Impact of mode of delivery and antibiotic exposure on breast milk microbiota according to maternal diet clusters. Multivariate redundancy discriminant analysis (RDA) showed significant differences in breast milk microbial communities between maternal diet clusters depending on antibiotic exposure and mode of delivery: vaginal/nonantibiotics (A) and C-section/antibiotics (B). Cluster I (n = 40) (green) and Cluster II (n = 72) (red). Breast milk microbiota profiles at phylum level (C) and at genus level (D) according to maternal diet cluster and mode of delivery and antibiotic exposure. C-section, cesarean section; PC1, principal component 1; VAG, vaginal delivery.

References

    1. Walker A Breast milk as the gold standard for protective nutrients. J Pediatr. 2010;156:S3–7.
    1. Lönnerdal B Bioactive proteins in human milk: mechanisms of action. J Pediatr. 2010;156:S26–30.
    1. Le Huërou-Luron I, Blat S, Boudry G. Breast- v. formula-feeding: impacts on the digestive tract and immediate and long-term health effects. Nutr Res Rev. 2010;23:23–36.
    1. Gomez-Gallego C, Garcia-Mantrana I, Salminen S, Collado MC. The human milk microbiome and factors influencing its composition and activity. Semin Fetal Neonatal Med. 2016;21:400–5.
    1. Soto A, Martín V, Jiménez E, Mader I, Rodríguez JM, Fernández L. Lactobacilli and Bifidobacteria in human breast milk: influence of antibiotherapy and other host and clinical factors. J Pediatr Gastroenterol Nutr. 2014;59:78–88.
    1. Turfkruyer M, Verhasselt V. Breast milk and its impact on maturation of the neonatal immune system. Curr Opin Infect Dis. 2015;28:199–206.
    1. Guaraldi F, Salvatori G. Effect of breast and formula feeding on gut microbiota shaping in newborns. Front Cell Infect Microbiol. [Internet]2012;2.doi:.
    1. Hennet T, Borsig L. Breastfed at Tiffany's. Trends Biochem Sci. 2016;41:1–11.
    1. Munblit D, Treneva M, Peroni DG, Colicino S, Chow LY, Dissanayeke S, Pampura A, Boner AL, Geddes DT, Boyle RJet al. . Immune components in human milk are associated with early infant immunological health outcomes: a prospective three-country analysis. Nutrients. [Internet]2017;9 doi:10.3390/nu9060532.
    1. Boix-Amorós A, Puente-Sánchez F, du Toit E, Linderborg KM, Zhang Y, Yang B, Salminen S, Isolauri E, Tamames J, Mira Aet al. . Mycobiome profiles in breast milk from healthy women depend on mode of delivery, geographic location, and interaction with bacteria. Appl Environ Microbiol. 2019;85:1–13.
    1. Hunt KM, Foster JA, Forney LJ, Schütte UME, Beck DL, Abdo Z, Fox LK, Williams JE, McGuire MK, McGuire MA. Characterization of the diversity and temporal stability of bacterial communities in human milk. PLoS One. 2011;6:1–8.
    1. Cabrera-Rubio R, Collado MC, Laitinen K, Salminen S, Isolauri E, Mira A. The human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. Am J Clin Nutr. 2012;96:544–51.
    1. Cabrera-Rubio R, Mira-Pascual L, Mira A, Collado MC. Impact of mode of delivery on the milk microbiota composition of healthy women. J Dev Orig Health Dis. 2016;7:54–60.
    1. Kumar H, du Toit E, Kulkarni A, Aakko J, Linderborg KM, Zhang Y, Nicol MP, Isolauri E, Yang B, Collado MCet al. . Distinct patterns in human milk microbiota and fatty acid profiles across specific geographic locations. Front Microbiol. [Internet]2016;7 10.3389/fmicb.2016.01619.
    1. Padilha M, Danneskiold-samsøe NB, Brejnrod A, Hoffmann C, Cabral VP, Iaucci J, de M, Sales CH, Fisberg RM, Cortez RV, Brix Set al. . The human milk microbiota is modulated by maternal diet. Microorganisms[Internet] 2019;7 doi:10.3390/microorganisms7110502.
    1. Moossavi S, Sepehri S, Robertson B, Bode L, Goruk S, Field CJ, Lix LM, de Souza RJ, Becker AB, Mandhane PJet al. . Composition and variation of the human milk microbiota are influenced by maternal and early-life factors. Cell Host Microbe. 2019;25:324–35.
    1. García-Mantrana I, Alcántara C, Selma-Royo M, Boix-Amorós A, Dzidic M, Gimeno-Alcañiz J, Úbeda-Sansano I, Sorribes-Monrabal I, Escuriet R, Gil-Raga Fet al. . MAMI: a birth cohort focused on maternal-infant microbiota during early life. BMC Pediatr. 2019;19:1–8.
    1. Salas-Salvadó J, Rubio MA, Barbany M, Moreno B, Grupo colaborativo de la SEEDO. Consenso SEEDO 2007 para la evaluación del sobrepeso y la obesidad y el establecimiento de criterios de intervención terapéutica. Medicina Clínica. 2007;128:184–96.. Spanish.
    1. Mouratidou T, Ford F, Fraser RB. Validation of a food-frequency questionnaire for use in pregnancy. Public Health Nutr. 2006;9:515–22.
    1. Cervera P, Farran A, Zamora-Ros R. Tablas de composición de alimentos del CESNID: Taules de composició d'aliments del CESNID. Rev Esp Salud Pública. 2004;78:407.
    1. Marlett JA, Cheung T-F. Database and quick methods of assessing typical dietary fiber intakes using data for 228 commonly consumed foods. J Am Diet Assoc. 1997;97:1139–51.
    1. Neveu V, Perez-Jiménez J, Vos F, Crespy V, Chaffaut L, Mennen L, Knox C, Eisner R, Cruz J, Wishart Det al. . Phenol-Explorer: an online comprehensive database on polyphenol contents in foods. Database. [Internet]2010. doi:10.1093/database/bap024.
    1. Tognon G, Nilsson LM, Lissner L, Johansson I, Hallmans G, Lindahl B, Winkvist A. The Mediterranean diet score and mortality are inversely associated in adults living in the subarctic region. J Nutr. 2012;142:1547–53.
    1. Voortman T, Kiefte-de Jong JC, Geelen A, Villamor E, Moll HA, de Jongste JC, Raat H, Hofman A, Jaddoe VWV, Franco OHet al. . The development of a diet quality score for preschool children and its validation and determinants in the generation R study. J Nutr. 2015;145:306–14.
    1. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JMet al. . Enterotypes of the human gut microbiome. Nature. 2011;473:174–80.
    1. McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.
    1. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K, Studer M, Roudier P. Cluster: cluster analysis basics and extensions. R Package version 2.1.0. R Foundation; 2019.
    1. Venables WN, Ripley BD. Modern applied statistics with S. Springer; 2002.
    1. Walesiak M, Dudek A. ClusterSim: searching for optimal clustering procedure for a data set. R Package version 0.48-3. R Foundation; 2019.
    1. Bougeard S, Dray S. Supervised multiblock analysis in R with the ade4. J Stat Soft. 2018;86:1–17.
    1. Boix-Amorós A, Collado MC, Mira A. Relationship between milk microbiota, bacterial load, macronutrients, and human cells during lactation. Front Microbiol. 2016;7:1–9.
    1. Cruaud P, Vigneron A, Lucchetti-Miganeh C, Ciron PE, Godfroy A, Cambon-Bonavita MA. Influence of DNA extraction method, 16S rRNA targeted hypervariable regions, and sample origin on microbial diversity detected by 454 pyrosequencing in marine chemosynthetic ecosystems. Appl Environ Microbiol. 2014;80:4626–39.
    1. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108:4516–22.
    1. García-Mantrana I, Selma-Royo M, González S, Parra-Llorca A, Martínez-Costa C, Collado MC. Distinct maternal microbiota clusters are associated with diet during pregnancy: impact on neonatal microbiota and infant growth during the first 18 months of life. Gut Microbes. 2020;11:962–78.
    1. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–7.
    1. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590.
    1. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. [Internet]2018. doi:10.1186/s40168-018-0605-2.
    1. Iwai S, Weinmaier T, Schmidt BL, Albertson DG, Poloso NJ, Dabbagh K, DeSantis TZ. Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes. PLoS One. 2016;11:e0166104.
    1. Zakrzewski M, Proietti C, Ellis JJ, Hasan S, Brion MJ, Berger B, Krause L. Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions. Bioinformatics. 2017;33:782–3.
    1. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson A, Moeller Set al. . gplots: various R programming tools for plotting data. R Foundation; 2019; [cited May 2020] [Internet]. Available from: .
    1. Murphy K, Curley D, O'Callaghan TF, O'Shea C, Dempsey EM, O'Toole PW, Ross RP, Ryan CA, Stanton C. The composition of human milk and infant faecal microbiota over the first three months of life : a pilot study. Sci Rep. 2017;7:1–10.
    1. Hermansson H, Kumar H, Collado MC, Salminen S, Isolauri E, Rautava S. Breast milk microbiota is shaped by mode of delivery and intrapartum antibiotic exposure. Front Nutr. 2019;6:1–8.
    1. Lundgren SN, Madan JC, Karagas MR, Morrison HG, Hoen AG, Christensen BC. Microbial communities in human milk relate to measures of maternal weight. Front Microbiol. 2019;10:1–15.
    1. Martín V, Maldonado-Barragán A, Moles L, Rodriguez-Baños M, del Campo R, Fernández L, Rodríguez JM, Jiménez E. Sharing of bacterial strains between breast milk and infant feces. J Hum Lact. 2012;28:36–44.
    1. Collado MC, Laitinen K, Salminen S, Isolauri E. Maternal overweight and excessive weight gain during pregnancy modify the immunomodulatory potential of breast milk. Pediatr Res. 2012;72:77–85.
    1. Lackey KA, Williams JE, Meehan CL, Zachek JA, Benda ED, Price WJ, Foster JA, Sellen DW, Kamau-Mbuthia EW, Kamundia EWet al. . What's normal? Microbiomes in human milk and infant feces are related to each other but vary geographically: the INSPIRE Study. Front Nutr. [Internet]2019;6 doi:10.3389/fnut.2019.00045.
    1. Tan SS, Khor GL, Stoutjesdijk E, Ng KWT, Khouw I, Bragt M, Schaafsma A, Dijck-brouwer DAJ, Muskiet FAJ. Case study of temporal changes in maternal dietary intake and the association with breast milk mineral contents. J Food Compos Anal. 2020;89:103468.
    1. Moro GE, Bertino E, Bravi F, Tonetto P, Gatta A, Quitadamo PA, Salvatori G, Profeti C, Di Nicola P, Decarli Aet al. . Adherence to the traditional Mediterranean diet and human milk composition: rationale, design, and subject characteristics of the MEDIDIET study. Front Pediatr. [Internet]2019;7 doi:10.3389/fped.2019.00066.
    1. Albesharat R, Ehrmann MA, Korakli M, Yazaji S, Vogel RF. Phenotypic and genotypic analyses of lactic acid bacteria in local fermented food, breast milk and faeces of mothers and their babies. Syst Appl Microbiol. 2011;34:148–55.
    1. Williams JE, Carrothers JM, Lackey KA, Beatty NF, York MA, Brooker SL, Shafii B, Price WJ, Settles ML, McGuire MAet al. . Human milk microbial community structure is relatively stable and related to variations in macronutrient and micronutrient intakes in healthy lactating women. J Nutr. 2017;147:1739–48.
    1. Faraldo Corrêa TA, Rogero MM, Mariko Hassimotto AN, Lajolo FM. The two-way polyphenols-microbiota interactions and their effects on obesity and related metabolic diseases. Front Nutr. [Internet]2019;6 doi:10.3389/fnut.2019.00188.
    1. Lee HC, Jenner AM, Low CS, Lee YK. Effect of tea phenolics and their aromatic fecal bacterial metabolites on intestinal microbiota. Res Microbiol. 2006;157:876–84.
    1. Ozdal T, Sela DA, Xiao J, Boyacioglu D, Chen F, Capanoglu E. The reciprocal interactions between polyphenols and gut microbiota and effects on bioaccessibility. Nutrients. 2016;8:78.
    1. García-Mantrana I, Calatayud M, Romo-Vaquero M, Espín JC, Selva M V, Collado MC. Urolithin metabotypes can determine the modulation of gut microbiota in healthy individuals by tracking walnuts consumption over three days. Nutrients. 2019;11:2483.
    1. Dzidic M, Mira A, Artacho A, Abrahamsson TR, Jenmalm MC, Collado MC. Allergy development is associated with consumption of breastmilk with a reduced microbial richness in the first month of life. Pediatr Allergy Immunol. 2020;31:250–7.
    1. Rutayisire E, Huang K, Liu Y, Tao F. The mode of delivery affects the diversity and colonization pattern of the gut microbiota during the first year of infants’ life: a systematic review. BMC Gastroenterol. [Internet]2016;16 doi:10.1186/s12876-016-0498-0.
    1. Sakwinska O, Moine D, Delley M, Combremont S, Rezzonico E, Descombes P, Vinyes-Pares G, Zhang Y, Wang P, Thakkar SK. Microbiota in breast milk of Chinese lactating mothers. PLoS One. 2016;11:e0160856.
    1. Urbaniak C, Angelini M, Gloor GB, Reid G. Human milk microbiota profiles in relation to birthing method, gestation and infant gender. Microbiome. [Internet]2016;4 doi:10.1186/s40168-015-0145-y.
    1. Urbaniak C, McMillan A, Angelini M, Gloor GB, Sumarah M, Burton JP, Reid G. Effect of chemotherapy on the microbiota and metabolome of human milk, a case report. Microbiome. [Internet]2014;2:24 doi:10.1186/2049-2618-2-24.
    1. Van Den Elsen LWJ, Garssen J, Burcelin R, Verhasselt V. Shaping the gut microbiota by breastfeeding: the gateway to allergy prevention?. Front Pediatr. [Internet]2019;7 10.3389/fped.2019.00047.
    1. Grönlund M, Gueimonde M, Laitinen K, Kociubinski G, Grönroos T, Salminen S, Isolauri E. Maternal breast-milk and intestinal bifidobacteria guide the compositional development of the Bifidobacterium microbiota in infants at risk of allergic disease. Clin Exp Allergy. 2007;37:1764–72.
    1. Lundgren SN, Madan JC, Emond JA, Morrison HG, Christensen BC, Karagas MR, Hoen AG. Maternal diet during pregnancy is related with the infant stool microbiome in a delivery mode-dependent manner. Microbiome. [Internet]2018;6 doi:org/10.1186/s40168-018-0490-8.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel