Abstract
Persistent organic pollutants (POPs) are compounds that are recalcitrant and ubiquitous that bioaccumulate in human milk (HM) and can impact infant growth and development. We explore the association between POP concentration in HM at 2–50 days postpartum and infant growth and development trajectory throughout the first year of life. A cohort of 68 healthy adult Brazilian women and their infants were followed from 28 to 35 gestational weeks to 12 months postpartum. HM samples were collected between 2 and 50 days postpartum, and POP concentrations were analyzed using gas chromatography with mass spectrometry. Concentrations of POPs >limit of quantification (LOQ) were defined as presence, and concentrations ≤LOQ as an absence. Growth z-scores were analyzed according to WHO growth charts and infant development scores according to Age & Stages Questionnaires at 1 (n = 66), 6 (n = 50), and 12 months (n = 45). Linear mixed effects (LME) models were used to investigate the association of POPs in HM with infant growth and development. Benjamini-Hochberg (BH) correction for multiple testing was performed to reduce the false discovery ratio. P < 0.1 was considered for models with the interaction between POPs and time/sex. After BH correction, adjusted LME models with time interaction showed (1) a positive association between the presence of β hexachlorocyclohexane and an increase in head circumference-for-age z-score (β = 0.003, P = 0.095); (2) negative associations between total POPs (β = −0.000002, P = 0.10), total organochlorine pesticides (β = −0.000002, P = 0.10), and dichlorodiphenyldichloroethylene concentrations in HM (β = −0.000002, P = 0.10) and fine motor scores. No statistical difference between the sexes was observed. Postnatal exposure to organochlorine pesticides in HM shows a positive association with the trajectory of head circumference-for-age z-score and a negative association with the trajectories of fine motor skills scores. Future studies on POP variation in HM at different postpartum times and their effect on infant growth and development should be encouraged.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All datasets used and/or, codebook, and analytic code generated during the current study are available from the corresponding author upon reasonable request pending application and approval.
References
Aerts R, Van Overmeire I, Colles A, Andjelković M, Malarvannan G, Poma G, Den Hond E, Van de Mieroop E, Dewolf MC, Charlet F, Van Nieuwenhuyse A, Van Loco J, Covaci A (2019) Determinants of persistent organic pollutant (POP) concentrations in human breast milk of a cross-sectional sample of primiparous mothers in Belgium. Environ Int 131:104979. https://doi.org/10.1016/j.envint.2019.104979
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723. https://doi.org/10.1109/TAC.1974.1100705
Altman DG, Bland JM (2003) Interaction revisited: the difference between two estimates. BMJ 326:219. https://doi.org/10.1136/bmj.326.7382.219
Balasundaram P, Avulakunta I (2023) StatPearls, Human growth and development http://www.ncbi.nlm.nih.gov/books/nbk567767/ Accessed 10 Mar 2023
Ballard O, Morrow AL (2013) Human milk composition: nutrients and bioactive factors. Pediatr Clin N Am 60:49–74. https://doi.org/10.1016/j.pcl.2012.10.002
Barbieri P, Crivellenti LC, Nishimura RY, Sartorelli DS (2015) Validation of a food frequency questionnaire to assess food group intake by pregnant women. J Hum Nutr Diet 28(Suppl 1):38–44. https://doi.org/10.1111/jhn.12224
Batalha MA, Ferreira ALL, Freitas-Costa NC, Figueiredo ACC, Carrilho TRB, Shahab-Ferdows S, Hampel D, Allen LH, Pérez-Escamilla R, Kac G (2021) Factors associated with longitudinal changes in B-vitamin and choline concentrations of human milk. Am J Clin Nutr 114:1560–1573. https://doi.org/10.1093/ajcn/nqab191
Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125:279–284. https://doi.org/10.1016/s0166-4328(01)00297-2
Berger PK, Ong ML, Bode L, Belfort MB (2023) Human milk oligosaccharides and infant neurodevelopment: a narrative review. Nutrients 15:719. https://doi.org/10.3390/nu15030719
Boucher O, Simard MN, Muckle G, Rouget F, Kadhel P, Bataille H, Chajès V, Dallaire R, Monfort C, Thomé JP, Multigner L, Cordier S (2013) Exposure to an organochlorine pesticide (chlordecone) and development of 18-month-old infants. Neurotoxicology 35:162–168. https://doi.org/10.1016/j.neuro.2013.01.007
Brasil (2005) RESOLUÇÃO-RDC N° 326, DE 9 DE NOVEMBRO DE 2005. Resolution of the Board of Directors No. 326, 9 NOVEMBER 2005. In: Ministério da Saúde – Agência de Vigilância Sanitária (ANVISA) https://bvsms.saude.gov.br/bvs/saudelegis/anvisa/2005/rdc0263_22_09_2005.html, (in Portuguese) Accessed 10 Aug. 2022
Brasil (2007) Banco de leite humano: funcionamento, prevenção e controle de riscos. Human milk bank: functioning, prevention and risk control. Agência Nacional de Vigilância Sanitária (ANVISA) Brasília. https://www.gov.br/anvisa/pt-br/centraisdeconteudo/publicacoes/servicosdesaude/publicacoes/manual-para-bancos-de-leite-humano.pdf, (in Portuguese) Accessed 15 Mar 2019
Brasil (2009) Lei N° 11.936, de 14 de maio de 2009. Law No. 11,936, of May 14, 2009. Proíbe a fabricação, a importação, a exportação, a manutenção em estoque, a comercialização e o uso de diclorodifeniltricloretano (DDT) e dá outras providências. In: Brasil. Diário Oficial da República Federativa do Brasil, Brasília https://legislacao.presidencia.gov.br/atos/?tipo=LEI&numero=11936&ano=2009&ato=bb4o3ZE90dVpWT28b, (in Portuguese) Accessed 10 Aug. 2022
Braun JM (2017) Early-life exposure to EDCs: role in childhood obesity and neurodevelopment. Nat Rev Endocrinol 13:161–173. https://doi.org/10.1038/nrendo.2016.186
Chambers JM, Cleveland WS, Kleiner B, Tukey PA (1983) Graphical methods for data analysis. Duxbury Press, Boston, MA, pp 11–16
Committee on Obstetric Practice, the American Institute of Ultrasound in Medicine, and the Society for Maternal-Fetal Medicine (2017) Committee Opinion No 700: Methods for Estimating the Due Date. Obstet Gynecol 129:e150–e154. https://doi.org/10.1097/aog.0000000000002046
Criswell R, Lenters V, Mandal S, Stigum H, Iszatt N, Eggesbø M (2017) Persistent environmental toxicants in breast milk and rapid infant growth. Ann Nutr Metab 70:210–216. https://doi.org/10.1159/000463394
Dewan P, Jain V, Gupta P, Banerjee BD (2013) Organochlorine pesticide residues in maternal blood, cord blood, placenta, and breastmilk and their relation to birth size. Chemosphere 90:1704–1710. https://doi.org/10.1016/j.chemosphere.2012.09.083
Du J, Gridneva Z, Gay MCL, Trengove RD, Hartmann PE, Geddes DT (2017) Pesticides in human milk of Western Australian women and their influence on infant growth outcomes: a cross-sectional study. Chemosphere 167:247–254. https://doi.org/10.1016/j.chemosphere.2016.10.005
Erick M (2018) Breast milk is conditionally perfect. Med Hypotheses 111:82–89. https://doi.org/10.1016/j.mehy.2017.12.020
Ferreira AL, Alves R, Figueiredo A, Alves-Santos N, Freitas-Costa N, Batalha M, Yonemitsu C, Manivong N, Furst A, Bode L, Kac G (2020) Human milk oligosaccharide profile variation throughout postpartum in healthy women in a Brazilian cohort. Nutrients 12. https://doi.org/10.3390/nu12030790
Ferreira ALL, Freitas-Costa N, Freire SSR, Figueiredo ACC, Padilha M, Alves-Santos NH, Kac G (2023) Association of pre-pregnancy maternal overweight/obesity and dietary intake during pregnancy with the concentrations of persistent organic pollutants in the human milk of women from Rio de Janeiro, Brazil. Environ Sci Pollut Res Int. https://doi.org/10.1007/s11356-023-25308-x
Gao W, Lin W, Grewen K, Gilmore JH (2017) Functional connectivity of the infant human brain. Neuroscientist 23:169–184. https://doi.org/10.1177/1073858416635986
Gilmore JH, Knickmeyer RC, Gao W (2018) Imaging structural and functional brain development in early childhood. Nat Rev Neurosci 19(3):123–137. https://doi.org/10.1038/nrn.2018.1
Govarts E et al (2012) Birth weight and prenatal exposure to polychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (DDE): a meta-analysis within 12 European Birth Cohorts. Environ Health Perspect 120:162–170. https://doi.org/10.1289/ehp.1103767
Grova N, Schroeder H, Olivier JL, Turner JD (2019) Epigenetic and neurological impairments associated with early life exposure to persistent organic pollutants. Int J Genomics 14(2019):2085496. https://doi.org/10.1155/2019/2085496
INTERGROWTH-21st AG (2012) ANTHROPOMETRY HANDBOOK. The International Fetal and Newborn Growth Consortium. https://media.tghn.org/articles/Anthropometry_Handbook_April_2012.pdf Accessed 10 Aug. 2019
Johnson W, Choh AC, Soloway LE, Czerwinski SA, Towne B, Demerath EW (2012) Eighty-year trends in infant weight and length growth: the Fels Longitudinal Study. J Pediatr 160:762–768. https://doi.org/10.1016/j.jpeds.2011.11.002
Kao CC, Que DE, Bongo SJ, Tayo LL, Lin YH, Lin CW, Lin SL, Gou YY, Hsu WL, Shy CG, Huang KL, Tsai MH, Chao HR (2019) Residue levels of organochlorine pesticides in breast milk and its associations with cord blood thyroid hormones and the offspring’s neurodevelopment. Int J Environ Res Public Health 16. https://doi.org/10.3390/ijerph16081438
Kim MJ, Pelloux V, Guyot E, Tordjman J, Bui LC, Chevallier A, Forest C, Benelli C, Clément K, Barouki R (2012) Inflammatory pathway genes belong to major targets of persistent organic pollutants in adipose cells. Environ Health Perspect 120(4):508–514. https://doi.org/10.1289/ehp.1104282
Klocke C, Lein PJ (2020) Evidence implicating non-dioxin-like congeners as the key mediators of polychlorinated biphenyl (PCB) developmental neurotoxicity. Int J Mol Sci 21:1013. https://doi.org/10.3390/ijms21031013
Koponen J, Rantakokko P, Airaksinen R, Kiviranta H (2013) Determination of selected perfluorinated alkyl acids and persistent organic pollutants from a small volume human serum sample relevant for epidemiological studies. J Chromatogr A 1309:48–55. https://doi.org/10.1016/j.chroma.2013.07.064
Lamat H, Sauvant-Rochat MP, Tauveron I, Bagheri R, Ugbolue UC, Maqdasi S, Navel V, Dutheil F (2022) Metabolic syndrome and pesticides: a systematic review and meta-analysis. Environ Pollut 305:119288. https://doi.org/10.1016/j.envpol.2022.119288
Lee S, Lee DK (2018) What is the proper way to apply the multiple comparison test?. Korean J Anesthesiol 71(5):353–360. https://doi.org/10.4097/kja.d.18.00242
Lu C, Cuartas J, Fink G, Mccoy D, Liu K, Li Z, Daelmans B, Richter L (2020) Inequalities in early childhood care and development in low/middle-income countries: 2010–2018. BMJ Glob Health 5:e002314. https://doi.org/10.1136/bmjgh-2020-002314
Martins J, Mochel FR (2022) Main spills of diesel oil and fuel oils in mangrove ecosystems in Brazil, from 1975 to 2022. Int J Biol Nat Sci:1–13. https://doi.org/10.22533/at.ed.813272220103
Mendes V, Ribeiro C, Delgado I, Peleteiro B, Aggerbeck M, Distel E, Annesi-Maesano I, Sarigiannis D, Ramos E (2021) The association between environmental exposures to chlordanes, adiposity and diabetes-related features: a systematic review and meta-analysis. Sci Rep 11. https://doi.org/10.1038/s41598-021-93868-4
Müller MHB, Polder A, Brynildsrud OB, Grønnestad R, Karimi M, Lie E, Manyilizu WB, Mdegela RH, Mokiti F, Murtadha M, Nonga HE, Skaare JU, Solhaug A, Lyche JL (2019) Prenatal exposure to persistent organic pollutants in Northern Tanzania and their distribution between breast milk, maternal blood, placenta and cord blood. Environ Res 170:433–442. https://doi.org/10.1016/j.envres.2018.12.026
Mullerova D, Pesta M, Dvorakova J, Cedikova M, Kulda V, Dvorak P, Bouchalová V, Kralickova M, Babuska V, Kuncova J, Langmajerova J, Muller L (2017) Polychlorinated biphenyl 153 in lipid medium modulates differentiation of human adipocytes. Physiol Res 66(4):653–662. https://doi.org/10.33549/physiolres.933280
Nakajima S, Saijo Y, Miyashita C, Ikeno T, Sasaki S, Kajiwara J, Kishi R (2017) Sex-specific differences in effect of prenatal exposure to dioxin-like compounds on neurodevelopment in Japanese children: Sapporo cohort study. Environ Res 159:222–231. https://doi.org/10.1016/j.envres.2017.08.006
Pereira-Fernandes A, Dirinck E, Dirtu AC, Malarvannan G, Covaci A, Van Gaal L, Vanparys C, Jorens PG, Blust R (2014) Expression of obesity markers and persistent organic pollutants levels in adipose tissue of obese patients: reinforcing the obesogen hypothesis? PLoS One 9(1):e84816. https://doi.org/10.1371/journal.pone.0084816
Porta M et al (2022) Plasma concentrations of persistent organic pollutants and pancreatic cancer risk. Int J Epidemiol 51:479–490. https://doi.org/10.1093/ije/dyab115
Qi SY, Xu XL, Ma WZ, Deng SL, Lian ZX, Yu K (2022) Effects of organochlorine pesticide residues in maternal body on infants. Front Endocrinol (Lausanne) 13:890307. https://doi.org/10.3389/fendo.2022.890307
Ramírez V, Gálvez-Ontiveros Y, González-Domenech PJ, Baca M, Rodrigo L, Rivas A (2022) Role of endocrine disrupting chemicals in children’s neurodevelopment. Environ Res 203:111890. https://doi.org/10.1016/j.envres.2021.111890
Rios-Leyvraz M, Yao Q (2023) The volume of breast milk intake in infants and young children: a systematic review and meta-analysis. Breastfeed Med 18(3):188–197. https://doi.org/10.1089/bfm.2022.0281
Rovira J, Martínez M, Mari M, Cunha SC, Fernandes JO, Marmelo I, Marques A, Haug LS, Thomsen C, Nadal M, Domingo JL, Schuhmacher M (2022) Mixture of environmental pollutants in breast milk from a Spanish cohort of nursing mothers. Environ Int 166:107375. https://doi.org/10.1016/j.envint.2022.107375
Santos ASE, Moreira JC, Rosa ACS, Câmara VM, Azeredo A, Asmus CIRF, Meyer A (2022) Persistent organic pollutant levels in maternal and cord blood plasma and breast milk: results from the Rio Birth Cohort Pilot Study of Environmental Exposure and Childhood Development (PIPA Study). Int J Environ Res Public Health 20:778. https://doi.org/10.3390/ijerph20010778
Sauer B, VanderWeele TJ (2013) Use of Directed Acyclic Graphs. In: Velentgas P, Dreyer NA, Nourjah P et al (eds) Developing a protocol for observational comparative effectiveness research: a user’s guide. Agency for Healthcare Research and Quality (US), Rockville (MD) Jan. Supplement 2
Smolders R, Schramm KW, Nickmilder M, Schoeters G (2009) Applicability of non-invasively collected matrices for human biomonitoring. Environ Health 8:8. https://doi.org/10.1186/1476-069x-8-8
Squires J, Twombly E, Bricker D, Potter L (2009) ASQ-3™ User's Guide. Brookes Publishing, Baltimore.
Takahashi T, Eguchi A, Watanabe M, Todaka E, Sakurai K, Mori C (2022) Association between telomere length in human umbilical cord tissues and polychlorinated biphenyls in maternal and cord serum. Chemosphere 300:134560. https://doi.org/10.1016/j.chemosphere.2022.134560
Team RC (2020) R: A language and environment for statistical computing. Foundation for Statistical Computing, Vienna (Austria) http://www.R-project.org/ Accessed 13 November 2020
Textor J, Hardt J, Knüppel S (2011) DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology 22:745. https://doi.org/10.1097/ede.0b013e318225c2be
UNEP (2020) STOCKHOLM convention on persistent organic pollutants (POPS). http://www.pops.int/Portals/0/download.aspx?d=UNEP-POPS-COP-CONVTEXT-2021.English.pdf Accessed 26 Jan. 2022
Van Den Berg M, Kypke K, Kotz A, Tritscher A, Lee SY, Magulova K, Fiedler H, Malisch R (2017) WHO/UNEP global surveys of PCDDs, PCDFs, PCBs and DDTs in human milk and benefit-risk evaluation of breastfeeding. Arch Toxicol 91:83–96. https://doi.org/10.1007/s00204-016-1802-z
Villar J et al (2014) International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet 384:857–868. https://doi.org/10.1016/s0140-6736(14)60932-6
Wang SS, Lu AX, Cao LL, Ran XF, Wang YQ, Liu C, Yan CH (2022) Effects of prenatal exposure to persistent organic pollutants on neonatal outcomes: a mother-child cohort (Shanghai, China). Environ Res 203:111767. https://doi.org/10.1016/j.envres.2021.111767
WHO (1995) Physical status: the use and interpretation of anthropometry. Who library, Geneve https://apps.who.int/iris/bitstream/handle/10665/37003/WHO_TRS_854.pdf?sequence=1&isAllowed=y Accessed 10 Jan. 2021
WHO (2006) WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-forheight and body mass index-for-age : methods and developmen. WHO Press, Switzerland https://apps.who.int/iris/rest/bitstreams/51510/retrieve Accessed 10 Aug. 2019
WHO (2007) WHO child growth standards: head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age: methods and development. https://apps.who.int/iris/handle/10665/43706 Accessed 10 Aug. 2019
WHO (2017) GUIDELINE: Protecting, promoting and supporting breastfeeding in facilities providing maternity and newborn services. WHO Document Production Service, Geneve, p 136 https://apps.who.int/iris/rest/bitstreams/1091116/retrieve Accessed 10 Aug. 2019
WHO (2020) Improving early childhood development: WHO guideline. In: Improving Early Childhood Development: WHO Guideline. World Health Organization© World Health Organization 2020, Geneva https://apps.who.int/iris/rest/bitstreams/1271261/retrieve Accessed 13 Jan. 2023
Yalçın SS, Örün E, Yalçın S, Aykut O (2015) Organochlorine pesticide residues in breast milk and maternal psychopathologies and infant growth from suburban area of Ankara, Turkey. Int J Environ Health Res 25:364–372. https://doi.org/10.1080/09603123.2014.945515
Yang C, Fang J, Sun X, Zhang W, Li J, Chen X, Yu L, Xia W, Xu S, Cai Z, Li Y (2021) Prenatal exposure to organochlorine pesticides and infant growth: a longitudinal study. Environ Int 148:106374. https://doi.org/10.1016/j.envint.2020.106374
Yang X, Zhang M, Lu T, Chen S, Sun X, Guan Y, Zhang Y, Zhang T, Sun R, Hang B, Wang X, Chen M, Chen Y, Xia Y (2020) Metabolomics study and meta-analysis on the association between maternal pesticide exposome and birth outcomes. Environ Res 182:109087. https://doi.org/10.1016/j.envres.2019.109087
Acknowledgements
We gratefully acknowledge all cohort women and infants from the Public Health Center, where the data were collected. We also thank the support of the Carlos Chagas Filho Foundation for Research Support of Rio de Janeiro State-FAPERJ and the National Council for Scientific and Technological Development-CNPq.
Funding
This work was partly supported by the Carlos Chagas Filho Foundation for Research Support of Rio de Janeiro State-FAPERJ [grant nos. E-26/210.190/2014 (200585), E-26/010.002429/2019 (211560), and E-26/200.429/2020] and by the National Council for Scientific and Technological Development-CNPq (grant no. 409676/2016).
Author information
Authors and Affiliations
Contributions
Ana Lorena Ferreira and Gilberto Kac: study conception and design; Ana Lorena Ferreira, Nathalia Freitas-Costa, Amanda Figueredo, and Nadya Alves-Santos: conducted the research data collection; Ana Lorena Ferreira: analyzed the data and performed the statistical analysis and written the first draft of the manuscript; Gilberto Kac: supervision and funding acquisition to research. Ana Lorena Ferreira, Nathalia Freitas-Costa, Samary Freire, Amanda Figueredo, Marina Padilha, Nadya Alves-Santos, and Gilberto Kac contributed to the review and editing, provided a critical evaluation, and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval
This study followed the rules of the Declaration of Helsinki of 1975 and was approved by the Research Ethics Committee of the Municipal Secretariat of Health and Civil Defense of the State of Rio de Janeiro and the Maternity School of Rio de Janeiro Federal University.
Consent to participate
All participants gave written consent on began of the research period, and the privacy rights of participants were guaranteed.
Consent for publication
All authors have approved the names and order of authors and the manuscript before submission.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Lotfi Aleya
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
ESM 1
Supplementary Fig. 1 Study theoretical model with a directed acyclic graph for minimal adjustment to the association between POPs in human milk and infant growth. Breastfeeding status indicates exclusively dichotomous yes/no. Note: POPs, persistent organic pollutants; PP-BMI, pre-pregnancy body mass index; GWG, total gestational weight gain; HM, human milk. Minimal adjustment: birth weight, breastfeeding status, total gestational weight gain, gestational age birth, maternal serum leptin, maternal education, PP- BMI. (TIF 888 kb)
ESM 2
Supplementary Fig. 2 Study theoretical model with a directed acyclic graph for minimal adjustment to the association between POPs in human milk and infant development. Breastfeeding status indicates exclusively dichotomous yes/no. Note: POPs, persistent organic pollutants; PP-BMI, pre-pregnancy body mass index; GWG, total gestational weight gain; HM, human milk. Minimal adjustment: birth weight, congenital anomalies, GWG, gestational age birth, gestational energy intake (kcal), maternal education, maternal age, PP-BMI, parity, smoking during pregnancy. (TIF 1080 kb)
ESM 3
Supplementary Fig. 3 Longitudinal linear mixed-effect models for z-scores growth trajectory throughout one (n=66), six (n=50), and 12 months (n=45) of infants’ life. Note: The space between oranges' dashed lines represents the normal range according to the World Health Organization growth chart (2006; 2007). The model coefficients, standard error (SE), and p-value for each z-score are as follows: weight for age: β= 0.001, SE= 0.0003, P=0.012 (A); length for age: β= 0.0001, SE= 0.0003, P=0.844 (B); BMI for age: β= 0.001, SE= 0.0004, P=0.002 (C); weight for length: β= 0.001, SE= 0.0004, P=0.047 (D); head circumference for age: β= 0.0002, SE= 0.0003, P=0.590 (E). (TIFF 10295 kb)
ESM 4
Supplementary Fig. 4 Longitudinal linear mixed-effect models for infant development scores throughout one (n=66), six (n=50), and 12 months (n=45) of infants’ life. Note: Score domains according to Age & Stages Questionnaires (Squires et al. 2009). The model coefficients, standard error (SE), and P for each z-score are as follows: (A) communication: β= 0.043, SE= 0.006, P<0.001; (B) gross motor skills: β= 0.050, SE= 0.006, P<0.001; (C) fine motor skills: β= 0.023, SE= 0.005, P<0.001; (D) personal-social: β= -0.0046, SE= 0.006, P=0.444; (E) problem solving: β= 0.004, SE= 0.0008, P<0.001. (TIFF 10295 kb)
ESM 5
Supplementary Fig. 5 Longitudinal linear mixed-effect models for infant growth z-scores throughout one, six, and 12 months of infants’ life in interaction with sex. Note: Boys: one (n=32), six (n=24), and 12 months (n=21). Girls: one (n=36), six (n=26), and 12 months (n=24). The model coefficients, standard error (SE), and p-value for each z-score are as follows: (A) weight-for-age (β =-0.00003, SE= 0.001, P=0.965), (B) height/length-for-age (β =0.0002, SE= 0.001, P=0.732), (C) BMI-for-age (β =-0.0004, SE= 0.0008, P=0.587), (D) head circumference-for-age (β =0.0009, SE= 0.0006, P=0.154), (E) weight-for-length (β =-0.001, SE= 0.001, P=0.274). (TIFF 774 kb)
ESM 6
Supplementary Fig. 6 Longitudinal linear mixed-effect models for infant development scores throughout one, six, and 12 months of infants’ life in interaction with sex. Note: Score domains according to Age & Stages Questionnaires (Squires et al. 2009). Boys: one (n=32), six (n=24), and 12 months (n=21). Girls: one (n=36), six (n=26), and 12 months (n=24). The model coefficients, standard error (SE), and p-value for each score are as follows: (A) communication (β =-0.00003, SE= 0.012, P= 0.980), (B) gross motor skills (β =-0.002, SE= 0.013, P= 0.878), (C) fine motor skills (β =0.004, SE= 0.010, P= 0.669), (D) problem-solving (β =-0.014, SE= 0.015, P= 0.346), and (E) personal-social (β =0.028, SE= 0.011, P=0.016). (TIFF 706 kb)
ESM 7
Supplementary Fig. 7 Longitudinal linear mixed-effect models for Personal-social infant development domain throughout the first year of infants’ life for (A) boys and (B) girls. Note: Score domains according to Age & Stages Questionnaires (Squires et al. 2009). Boys: one (n=32), six (n=24), and 12 months (n=21). Girls: one (n=36), six (n=26), and 12 months (n=24). The model coefficients, standard error (SE), and p-value: (A) Boys: β=0.0006, SE= 0.0003, P= 0.595; (B) Girls: β=0.0004, SE=0.0002, P= 0.786. (TIFF 17158 kb)
ESM 8
Supplementary Table 1. Linear mixed models between POPs and weight-for-age (WAZ), length-for-age (HAZ), BMI-for-age (BAZ), weight for length (WHAZ) and head circumference-for-age z-scores in the first year of infant life in unadjusted models, with the interaction between POPs concentration and days postpartum (*days) and with interaction adjusted (*days adj.). Supplementary Table 2. Linear mixed models between POPs and communication, gross motor skills, fine motor skills, problem solving and personal-social scores in the first year of infant life in unadjusted models, with the interaction between POPs concentration and days postpartum (*days) and with interaction adjusted (*days adj.).(XLSX 45 kb)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ferreira, .L.L., Freitas-Costa, N., da Silva Rosa Freire, S. et al. Association between persistent organic pollutants in human milk and the infant growth and development throughout the first year postpartum in a cohort from Rio de Janeiro, Brazil. Environ Sci Pollut Res 30, 115050–115063 (2023). https://doi.org/10.1007/s11356-023-30316-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-30316-y