Lead contamination in human milk affects infants’ language trajectory: results from a prospective cohort study

Infants growing up in low- and middle-income countries are at increased risk of suffering adverse childhood experiences, including exposure to environmental pollution and lack of cognitive stimulation. In this study, we aimed to examine the levels of metals in the human milk of women living in São Paulo City, Brazil, and determine the effects on infants’ neurodevelopment. For such, a total of 185 human milk samples were analyzed for arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd) using inductively coupled plasma mass spectrometry (ICP-MS). We applied the Bayley scales of infant and toddler development Third Edition (Bayley-III) to assess developmental milestones. In our analysis, we found a mean (standard deviation) concentration of As in human milk equal to 2.76 (4.09) μg L⁻¹, followed by Pb 2.09 (5.36) and Hg 1.96 (6.68). Cd was not detected. We observed that infants exposed to Pb presented language trajectories lower than non-exposed infants (β = −0.413; 95% CI -0.653, −0.173) after adjustment for infant age, maternal education, socioeconomic status, infant sex, and sample weights. Our results report As, Pb, and Hg contamination in human milk, and that infant exposure to Pb decreased infants’ language development. These results evidence maternal-child environmental exposure and its detrimental impact on infants’ health.

1 Introduction

Infants in low- and middle-income countries (LMICs) have the highest risk of being exposed to environmental pollution (1). According to the Lancet Commission on Pollution and Health (2017), there is a significant inequity in pollution-related deaths, with the highest burden in LMICs (2). The Commission reported that pollution was responsible for approximately 9 million deaths (16% of all deaths globally) in 2015, and more than 90% of these deaths occurred in LMICs (2). Pollution includes the contamination of air by fine particulate matter (PM2.5); the contamination of the ocean by mercury, nitrogen, plastic, and petroleum waste; and the poisoning of the land by lead, mercury, pesticides, industrial chemicals, and electronic waste, among others (3).

Exposure to environmental pollutants can be particularly dangerous for infants, as their metabolic capacity and biochemical pathways are immature, and their organs and systems are still developing (4). These contaminants represent a significant risk for exclusively breastfed infants since human milk can contain contaminants, due to environmental exposure being their only source of nutrition during the initial months of life (5). Toxic exposures before birth or in early postnatal life can lead to short-term death in infancy and childhood, as well as chronic non-communicable diseases (NCDs), including neurobehavioral disorders (6), that may manifest at any point throughout the human lifespan (6, 7).

Epidemiological evidence has highlighted the risks associated with environmental exposures that may result in alterations to brain development (8–11). Understanding the impact of exposures during the first 1,000 days can shed light on brain development in LMIC populations. Furthermore, metal exposure and its association with neurodevelopment are poorly understood and require further confirmation in different human populations. This study aimed to investigate the association between infants’ exposure to arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd) via human milk and adverse neurodevelopmental outcomes.

2 Materials and methods

We conducted a longitudinal analysis on the Germina cohort (12), a population-based infant cohort from the metropolitan area of São Paulo. One hundred and eighty-five healthy mother-infant dyads provided human milk samples at 3 months of age. Infants’ neurodevelopment was assessed at 3, 5–9, and 10–16 months using the Bayley scales of infant and toddler development 3rd Edition (Bayley-III). The Bayley-III consists of a series of tasks and behavioral observations, including the following domains: cognitive, language, motor, and social–emotional development. The Bayley-III was previously translated and adapted to Brazilian Portuguese, and we used the composite scores in this study (13).

Ethical approvals were obtained from the Ethics Committee for the Analysis of Research Projects (CAPPESq) and the National Council of Ethics in Research (ref.: CAAE 49671221.2.0000.0068). Following the Declaration of Helsinki, all mothers provided written informed consent before completing any study measure.

We analyzed the levels of As, Pb, Hg, and Cd in the samples using acid digestion with 65% nitric acid (w/v) and inductively coupled plasma mass spectrometry (ICP-MS) model NexION 300D, manufactured by PerkinElmer, United States. The ICP-MS system was equipped with a concentric nebulizer (Meinhard), cyclonic glass nebulizer chamber, cone, skimmer, and nickel hyper-skimmer technique at the National Institute of Quality Control in Health Laboratory (INCQS) (14). The limit of detection (LOD) was determined by analyzing 10 independent blank solutions and calculated according to the National Institute of Metrology, Standardization, and Industrial Quality (INMETRO) guidance document for a 95% confidence level (15). The accuracy and precision of the method were assessed through a recovery study. Their acceptance criteria typically range from 80 to 120% of the certified value, with a maximum relative standard deviation (% RSD) below 30% (15, 16). The metal’s limits of quantification (LOQ) were: Pb 0.015 μg/dL, Hg 0.007 μg L⁻¹, Cd 0.002 μg L⁻¹, As 0.003 μg L⁻¹. For values below the LOQ, we imputed the limit of detection (LOD) divided by two, as suggested for right-skewed data distribution (17). Since the imputation rate was higher than 50%, infants were categorized as exposed or non-exposed for further analysis It is essential to note that the group of infants categorized as “non-exposed to Pb” were not exposed to detectable levels of this metal in human milk samples according to the technique used. However, exposure below LOD and from other sources cannot be ruled out. To compare differences between infant groups we used chi-square test for categorical variables and t-test for r continuous variables. The metal levels of the exposed infants are presented as the mean (SD).

We used a linear mixed-effects model [nlme R package (18)] with repeated measures to assess the relationship between longitudinal language scores and Pb exposure, with the presence and absence of the interaction term between infant age and Pb exposure. The adjustment process incorporated key socio-economic variables, maternal ethnicity and education, socioeconomic status score, infant age, and sex. The socioeconomic status score was defined based on Brazil’s Criteria for Economic Classification. We constructed pseudo-weights (19) to improve the representativeness of our sample relative to the general population. All analyses were conducted in R.¹

3 Results

We evaluated the levels of metals in human milk samples (n = 185). The highest detection rate was for As (38.6%), followed by Hg (23.9%) and Pb (22.8%). The mean (SD) concentration was 2.76 (4.09) μg/L, 1.96 (6.68), 2.09 (5.36), respectively. Cd was not detected (Table 1).

Table 1

Table 1. Levels of metals in human milk.

Further, we analyzed the associations of As, Pb, Hg, and Pb exposure and the Bayley-III cognitive and language composite scores across time points, and only Pb showed a significant effect in the Bayley language composite trajectory (Supplementary Table 1). Therefore, the subsequent analyses are regarding Pb and the language Bayley Scale. Maternal and infant characteristics according to Pb exposure are described in Table 2. No significant differences were observed for total family income, maternal education, infant sex, gestational age, and socio-economic between the infant groups. Infants exposed to Pb had significantly lower (mean[sd]) performance in the language Bayley Scale at 10–16 months of age (97.47 [13.36]) compared to non-exposed infants (102.96 [11.53]).

Table 2

Table 2. Maternal and infants’ characteristics.

Table 3 shows the regression coefficients for infants’ language trajectory predictors. The joint effect of Pb exposure and infant age resulted in a decreased language trajectory only among the Pb exposed infant group (β = −0.413; 95% CI = −0.653, −0.173), adjusting for infant age, socioeconomic status, maternal education, infant sex, and sample weights (Figure 1). Socioeconomic status was also a significant predictor of language trajectory (β = 0.129; 95% CI = 0.002, 0.255).

Table 3

Table 3. Regression coefficients of language trajectories predictors.

Figure 1

Figure 1. Infants exposed to Pb had lower language trajectories compared to non-exposed infants. X-axis are infant age in days and Y-axis are infants language Bayley trajectory stratified by Pb exposure status.

4 Discussion

In this study, we measured the levels of metals in human milk and their association with infant language development during the first 2 years of life. We found that about one-third of the human milk samples had detectable Pb, As, and Hg levels but not Cd. Also, infants exposed to Pb via human milk showed lower language trajectories than non-exposed infants.

Epidemiological research has demonstrated that child neurodevelopment is inversely related to Pb exposure, measured either in maternal blood, cord blood, or human milk (11, 20, 21), although others have reported null associations (22, 23). Comparing these studies is difficult due to variations in the sampling timing, the method and age of the neurodevelopmental tests, the number of participants, and the statistical modeling techniques. For example, the Pb concentrations in human milk in our study were lower than in the majority of previous studies in other Brazilian states, Lebanon, and Spain (24–26) and yet, it was associated with lower language skills. Although the accepted Pb levels in human milk are between 2 and 5 μg/L (27), there are no known safe commendations for blood Pb levels (28). Even blood Pb concentrations as low as 3.5 μg/dL may be linked to decreased intelligence in children, behavioral difficulties, and learning problems (29).

In a Bangladesh cohort, increased blood lead levels in children aged 20 to 40 months were associated with decreased cognitive scores (30). In Suriname, prenatal lead exposure was associated with lower receptive and expressive communication scores in children aged 1 to 2 years (31). Rural and suburban Mexican infants had a 1.5-point decrease in language development for every 1 μg/dL increase in maternal blood lead levels (32). All assessments were conducted using the Bayley-III scale. This evidence was generated in low- to middle-income countries, with the last performed in rural and suburban areas. This highlights the vulnerability of these populations to environmental pollutants exposure, aligning with our findings that socioeconomic status was a significant predictor of language development in addition to population settings.

Infants are particularly vulnerable to absorbing metals since the intestinal barrier is immature, and these elements’ permeability may be increased (33). The immaturity of other organs and systems also contributes to higher toxicity during infancy (33). The brain is more likely to be affected by Pb and neurotoxicity occurs through multiple mechanisms. Pb can cross the blood–brain barrier (BBB) via calcium channels and accumulate in astrocytes. By replacing calcium in enzyme activities, Pb can damage the mitochondria and alter lipid metabolism and decrease neurotransmitter release (34, 35). Furthermore, Pb can replace zinc in processes that regulate genetic transcription, such as zinc-finger proteins or zinc-binding sites in receptor channels. Changes in mechanisms that control gene expression during early neurodevelopment could decrease gray matter, alter myelin, and potentially lead to neurological disorders in adults (35).

Pb has been extensively investigated in relation to autism spectrum disorders (ASD) (36). A meta-analysis showed that higher levels of Pb were found in several biological samples of autistic children compared to controls (37). This suggests a possible link between the development of ASD and environmental exposure to Pb. Possible mechanisms that could explain this association include the deregulation of physiological levels of neurotransmitters (35) and the production of serum anti-ribosomal P antibodies (8). In addition, a systematic review found that infants and children exposed to Pb experience reductions in acetylcholine, glutamate and GABA levels, and NMDA receptors expression levels, which results in the decline of their reading and language abilities, increased stress response, and poor as memory (38). Pb exposure has also been associated with aggravated behavioral and immune abnormalities in autistic mouse models (39).

Pb can accumulate in bone tissue and be released into the blood and soft tissue organs in periods of physiological stress (34), such as during infants’ growth spurts, which occur typically between 5 and 12 months of life (40). Although this could explain the later effect observed in the Bayley Language Score, we should also consider improving the Bayley Scale accuracy when used in ages greater than 12 months as a possible explanation.

Our study’s limitations should be mentioned. Due to Pb quantification being lower than 50%, it had to be analyzed dichotomously (exposed or non-exposed), which may miss subtle differences in toxicity and not allow for an investigation of a dose–response relationship. Dichotomous analysis can also result in less statistical power. Also, we cannot confirm that Pb was absorbed by the infant since Pb was assessed in human milk. Regarding the outcome assessment, although recommended, the Bayley scale might not be accurate when applied as early as 12 months of infant age. Finally, while our study design could demonstrate temporality, it was not intended to establish causality.

Our results deserve appreciation because they report an estimate of high effect size, even accounting for several covariates, including maternal education and family income, infant sex, and socioeconomic status. To address the sampling biases inherent in volunteer samples, we employed pseudo-sampling weights designed to improve the representativeness of our cohort. Benefiting from longitudinal data, we computed the infant language trajectory using repeated measures rather than analyzing multiple time points in a cross-sectional manner.

In conclusion, our study shows that exposure to Pb via human milk is associated with lower language trajectory during the first 2 years of life. This emphasizes the need to revise the currently accepted levels of metals in human samples, as even low concentrations could harm vulnerable populations. More research is required to confirm the link between lead exposure and neurodevelopmental deficits in early life and to determine long-term consequences in children and adults.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by the Ethics Committee for the Analysis of Research Projects (CAPPESq) and the National Council of Ethics in Research (ref. number CAAE 49671221.2.0000.0068). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

NN: Writing – original draft, Formal analysis, Data curation, Conceptualization. PV: Data curation, Conceptualization, Writing – original draft. PT: Data curation, Writing – review & editing. PS: Writing – review & editing. DF: Project administration, Methodology, Conceptualization, Writing – review & editing. SV: Writing – review & editing, Formal analysis. CB-S: Writing – review & editing, Formal analysis. LS: Writing – review & editing, Formal analysis. AF: Validation, Funding acquisition, Data curation, Writing – review & editing. MP-B: Funding acquisition, Writing – review & editing. PB-B: Funding acquisition, Writing – review & editing. AlC: Funding acquisition, Writing – review & editing. AnC: Funding acquisition, Writing – review & editing. GP: Funding acquisition, Writing – review & editing. JM: Resources, Funding acquisition, Writing – review & editing. CT: Writing – review & editing, Supervision, Funding acquisition.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by a multi-million-dollar, multi-year funding from the Wellcome Leap 1kD program (The First 1000 Days; https://wellcomeleap.org/1kd/).

Acknowledgments

The authors are grateful to the National Institute of Quality Control in Health Laboratory (INCQS) for the metals assessment, and the National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1450570/full#supplementary-material

Abbreviations

LMICs, Low- and middle-income countries; ICP-MS, Inductively coupled plasma mass spectrometry; LOQ, Limits of quantification; LOD, Limit of detection; GABA, Gamma-aminobutyric acid; NMDA, N-methyl D-aspartate.

Footnotes

1. ^http://www.r-project.org

References

1. Suk, WA, Ahanchian, H, Asante, KA, Carpenter, DO, Diaz-Barriga, F, Ha, E-H, et al. Environmental pollution: an under-recognized threat to Children’s health, especially in low- and middle-income countries. Environ Health Perspect. (2016) 124:A41–5. doi: 10.1289/ehp.1510517

PubMed Abstract | Crossref Full Text | Google Scholar

2. Landrigan, PJ, Fuller, R, Acosta, NJR, Adeyi, O, Arnold, R, Basu, N, et al. The lancet commission on pollution and health. Lancet. (2018) 391:462–512. doi: 10.1016/S0140-6736(17)32345-0

Crossref Full Text | Google Scholar

3. Fuller, R, Landrigan, PJ, Balakrishnan, K, Bathan, G, Bose-O’Reilly, S, Brauer, M, et al. Pollution and health: a progress update. Lancet Planet Health. (2022) 6:e535–47. doi: 10.1016/S2542-5196(22)00090-0

Crossref Full Text | Google Scholar

4. Landrigan, PJ, and Goldman, LR. Children’s vulnerability to toxic chemicals: a challenge and opportunity to strengthen health and environmental policy. Health Aff. (2011) 30:842–50. doi: 10.1377/hlthaff.2011.0151

PubMed Abstract | Crossref Full Text | Google Scholar

5. Chao, H-H, Guo, C-H, Huang, C-B, Chen, P-C, Li, H-C, Hsiung, D-Y, et al. Arsenic, cadmium, Lead, and Aluminium concentrations in human Milk at early stages of lactation. Pediatr Neonatol. (2014) 55:127–34. doi: 10.1016/j.pedneo.2013.08.005

PubMed Abstract | Crossref Full Text | Google Scholar

6. Grandjean, P, and Landrigan, PJ. Neurobehavioural effects of developmental toxicity. Lancet Neurol. (2014) 13:330–8. doi: 10.1016/S1474-4422(13)70278-3

PubMed Abstract | Crossref Full Text | Google Scholar

7. Boekelheide, K, Blumberg, B, Chapin, RE, Cote, I, Graziano, JH, Janesick, A, et al. Predicting later-life outcomes of early-life exposures. Environ Health Perspect. (2012) 120:1353–61. doi: 10.1289/ehp.1204934

PubMed Abstract | Crossref Full Text | Google Scholar

8. Mostafa, GA, Bjørklund, G, Urbina, MA, and Al-Ayadhi, LY. The positive association between elevated blood lead levels and brain-specific autoantibodies in autistic children from low lead-polluted areas. Metab Brain Dis. (2016) 31:1047–54. doi: 10.1007/s11011-016-9836-8

PubMed Abstract | Crossref Full Text | Google Scholar

9. Hamadani, JD, Grantham-McGregor, SM, Tofail, F, Nermell, B, Fangstrom, B, Huda, SN, et al. Pre- and postnatal arsenic exposure and child development at 18 months of age: a cohort study in rural Bangladesh. Int J Epidemiol. (2010) 39:1206–16. doi: 10.1093/ije/dyp369

Crossref Full Text | Google Scholar

10. Heng, YY, Asad, I, Coleman, B, Menard, L, Benki-Nugent, S, Hussein Were, F, et al. Heavy metals and neurodevelopment of children in low and middle-income countries: a systematic review. PLoS One. (2022) 17:e0265536. doi: 10.1371/journal.pone.0265536

PubMed Abstract | Crossref Full Text | Google Scholar

11. Marques, RC, Bernardi, JVE, Dórea, JG, de Fatima, R, Moreira, M, and Malm, O. Perinatal multiple exposure to neurotoxic (lead, methylmercury, ethylmercury, and aluminum) substances and neurodevelopment at six and 24 months of age. Environ Pollut. (2014) 187:130–5. doi: 10.1016/j.envpol.2014.01.004

PubMed Abstract | Crossref Full Text | Google Scholar

12. Fatori, D, Shephard, E, Naspolini, NF, Guzman, GC, Wang, JYT, Tótolo, P, et al. Identifying biomarkers and trajectories of executive functions and language development in the first 3 years of life: design, methods and initial findings of the Germina cohort study. OSF [Preprint] (2024) Available online at: https://osf.io/preprints/osf/ed4fb (Accessed June 12, 2024).

Google Scholar

13. Madaschi, V, Mecca, TP, Macedo, EC, and Paula, CS. Bayley-III scales of infant and toddler development: transcultural adaptation and psychometric properties. Paidéia. (2016) 26:189–97. doi: 10.1590/1982-43272664201606

Crossref Full Text | Google Scholar

14. Vicentini Neto, SA, Santos, LMG, Silva, CB, Fonseca, M, Magalhães, CD, Jacob, S, et al. Optimization and validation of an analytical methodology for the determination of as, cd, Pb and hg in blood matrix by inductively coupled plasma mass spectrometry (ICP-MS). Revista Virtual de Química. (2022) 14:896–901. doi: 10.21577/1984-6835.20220044

Crossref Full Text | Google Scholar

15. Instituto Nacional De Metrologia Qet. Orientação sobre validação de métodos analíticos—DOQ-CGCRE-008. Coordenação Geral de Acreditação (2020) Available online at: http://www.inmetro.gov.br/Sidoq/Arquivos/CGCRE/DOQ/DOQ-CGCRE-8_09.pdf (Accessed May 2, 2022).

Google Scholar

16. International Standard Organization. General requirements for the competence of testing and calibration laboratories. ISO/IEC 17025 (2017) Available online at: https://www.iso.org/publication/PUB100424.html (Accessed May 2, 2022).

Google Scholar

17. Krishnamoorthy, K, Mallick, A, and Mathew, T. Model-based imputation approach for data analysis in the presence of non-detects. Ann Occup Hyg. (2009) 53:249–63. doi: 10.1093/annhyg/men083

PubMed Abstract | Crossref Full Text | Google Scholar

18. Pinheiro, J, and Bates, D, R Core team. Nlme: linear and nonlinear mixed effects models. (2024) Available online at: https://CRAN.R-project.org/package=nlme (Accessed January 26, 2024).

Google Scholar

19. Elliott, MR, and Valliant, R. Inference for nonprobability samples. Stat Sci. (2017) 32:598. doi: 10.1214/16-STS598

Crossref Full Text | Google Scholar

20. Shah-Kulkarni, S, Ha, M, Kim, B-M, Kim, E, Hong, Y-C, Park, H, et al. Neurodevelopment in early childhood affected by prenatal Lead exposure and Iron intake. Medicine. (2016) 95:e2508. doi: 10.1097/MD.0000000000002508

PubMed Abstract | Crossref Full Text | Google Scholar

21. Al-Saleh, I, Moncari, L, Jomaa, A, Elkhatib, R, Al-Rouqi, R, Eltabache, C, et al. Effects of early and recent mercury and lead exposure on the neurodevelopment of children with elevated mercury and/or developmental delays during lactation: a follow-up study. Int J Hyg Environ Health. (2020) 230:113629. doi: 10.1016/j.ijheh.2020.113629

PubMed Abstract | Crossref Full Text | Google Scholar

22. Inoue, H, Sanefuji, M, Sonoda, Y, Ogawa, M, Hamada, N, Shimono, M, et al. No association between prenatal lead exposure and neurodevelopment during early childhood in the Japan environment and Children’s study. Sci Rep. (2022) 12:15305. doi: 10.1038/s41598-022-19509-6

PubMed Abstract | Crossref Full Text | Google Scholar

23. Wigg, NR, Vimpani, GV, McMichael, AJ, Baghurst, PA, Robertson, EF, and Roberts, RJ. Port Pirie cohort study: childhood blood lead and neuropsychological development at age two years. J Epidemiol Community Health. (1988) 42:213–9. doi: 10.1136/jech.42.3.213

Crossref Full Text | Google Scholar

24. Motas, M, Jiménez, S, Oliva, J, Cámara, MÁ, and Pérez-Cárceles, MD. Heavy metals and trace elements in human breast Milk from industrial/mining and agricultural zones of southeastern Spain. Int J Environ Res Public Health. (2021) 18:9289. doi: 10.3390/ijerph18179289

PubMed Abstract | Crossref Full Text | Google Scholar

25. Bassil, M, Daou, F, Hassan, H, Yamani, O, Kharma, JA, Attieh, Z, et al. Lead, cadmium and arsenic in human milk and their socio-demographic and lifestyle determinants in Lebanon. Chemosphere. (2018) 191:911–21. doi: 10.1016/j.chemosphere.2017.10.111

PubMed Abstract | Crossref Full Text | Google Scholar

26. Koyashiki, GAK, Paoliello, MMB, Matsuo, T, de Oliveira, MMB, Mezzaroba, L, de Fátima, CM, et al. Lead levels in milk and blood from donors to the breast Milk Bank in Southern Brazil. Environ Res. (2010) 110:265–71. doi: 10.1016/j.envres.2009.12.001

PubMed Abstract | Crossref Full Text | Google Scholar

27. World Health Organization, International Atomic Energy Agency. Minor and trace elements in breast milk: report of a joint WHO/IAEA collaborative study. World Health Organization. (1989). 159 p. Available online at: https://iris.who.int/handle/10665/39678 (Accessed April 12, 2024).

Google Scholar

28. World Health Organization. Lead poisoning. World Health Organization (2023) Available online at: https://www.who.int/health-topics/lead-poisoning#tab=tab_2 (Accessed April 12, 2024).

Google Scholar

29. US CDC Advisory Committee on Childhood Lead Poisoning Prevention. CDC updates blood lead reference value to 3.5μg/dL. Atlanta: US Centres for Disease Control and Prevention (2021) Available online at: https://www.cdc.gov/nceh/lead/news/cdc-updates-blood-lead-reference-value.html (Accessed February 13, 2024).

Google Scholar

30. Rodrigues, EG, Bellinger, DC, Valeri, L, Hasan, MOSI, Quamruzzaman, Q, Golam, M, et al. Neurodevelopmental outcomes among 2- to 3-year-old children in Bangladesh with elevated blood lead and exposure to arsenic and manganese in drinking water. Environ Health. (2016) 15:44. doi: 10.1186/s12940-016-0127-y

PubMed Abstract | Crossref Full Text | Google Scholar

31. Koendjbiharie, AP, Hindori-Mohangoo, AD, Zijlmans, WCWR, Wickliffe, JK, Shankar, A, Covert, HH, et al. The single and combined effects of prenatal nonchemical stressors and Lead exposure on neurodevelopmental outcomes in toddlers: results from the CCREOH environmental epidemiologic study in Suriname. Children. (2023) 10:287. doi: 10.3390/children10020287

Crossref Full Text | Google Scholar

32. Farías, P, Hernández-Bonilla, D, Moreno-Macías, H, Montes-López, S, Schnaas, L, Texcalac-Sangrador, JL, et al. Prenatal co-exposure to manganese, mercury, and Lead, and neurodevelopment in children during the first year of life. Int J Environ Res Public Health. (2022) 19:13020. doi: 10.3390/ijerph192013020

Crossref Full Text | Google Scholar

33. Lidsky, TI, and Schneider, JS. Lead neurotoxicity in children: basic mechanisms and clinical correlates. Brain. (2003) 126:5–19. doi: 10.1093/brain/awg014

Crossref Full Text | Google Scholar

34. Collin, MS, Venkatraman, SK, Vijayakumar, N, Kanimozhi, V, Arbaaz, SM, Stacey, RGS, et al. Bioaccumulation of lead (Pb) and its effects on human: a review. J Hazard Materials Adv. (2022) 7:100094. doi: 10.1016/j.hazadv.2022.100094

Crossref Full Text | Google Scholar

35. Gorini, F, Muratori, F, and Morales, MA. The role of heavy metal pollution in neurobehavioral disorders: a focus on autism. Rev J Autism Dev Disord. (2014) 1:354–72. doi: 10.1007/s40489-014-0028-3

Crossref Full Text | Google Scholar

36. Ng, M, de Montigny, J, Ofner, M, and Do, M. Environmental factors associated with autism spectrum disorder: a scoping review for the years 2003–2013. Health Promot Chronic Dis Prev Can. (2017) 37:1–23. doi: 10.24095/hpcdp.37.1.01

PubMed Abstract | Crossref Full Text | Google Scholar

37. Stojsavljević, A, Lakićević, N, and Pavlović, S. Does Lead have a connection to autism? A Systematic Review and Meta-Analysis. Toxics. (2023) 11:753. doi: 10.3390/toxics11090753

Crossref Full Text | Google Scholar

38. Goel, A, and Aschner, M. The effect of Lead exposure on autism development. Int J Mol Sci. (2021) 22:1637. doi: 10.3390/ijms22041637

PubMed Abstract | Crossref Full Text | Google Scholar

39. Almutairi, MM, Nadeem, A, Ansari, MA, Bakheet, SA, Attia, SM, Albekairi, TH, et al. Lead (Pb) exposure exacerbates behavioral and immune abnormalities by upregulating Th17 and NF-κB-related signaling in BTBR T+ Itpr3tf/J autistic mouse model. Neurotoxicology. (2022) 91:340–8. doi: 10.1016/j.neuro.2022.06.007

PubMed Abstract | Crossref Full Text | Google Scholar

40. Hvidt, JJ, Brix, N, Ernst, A, Lauridsen, LLB, and Ramlau-Hansen, CH. Size at birth, infant growth, and age at pubertal development in boys and girls. Clin Epidemiol. (2019) 11:873–83. doi: 10.2147/CLEP.S217388

Google Scholar