Contamination of breast milk with lead, mercury, arsenic, and cadmium in Iran: a systematic review and meta-analysis.
Salman MohammadiMaryam ShafieeSeyed Nooreddin FarajiMohsen RezaeianAli Ghaffarian-BahramanPublished in: Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine (2022)
Breast milk is a complete food for the development of the newborn, but it can also be an important route for environmental pollutants transmission to the infants. This study was aimed to evaluate the status of heavy metals including lead (Pb), mercury (Hg), cadmium (Cd) and arsenic (As) in the breast milk of Iranian mothers. The international databases including Scopus, PubMed, Web of Science and the Persian electronic databases including Scientific Information Database, IranMedex and Magiran were examined to find relevant articles published until July 2021. A total of 23 studies examined the levels of toxic metals in Iranian breast milk samples. According to the findings, the pooled average concentrations (µg/L) of Pb, Cd, Hg and As were 25.61, 2.40, 1.29 and 1.16, respectively. The concentration of Hg and Pb in colostrum milk was more than twice of mature milk. The Hg mean concentration in the breast milk of mothers with at least one amalgam-filled tooth was approximately three times that of mothers without amalgam-filled teeth. Risk assessment analysis indicated that the intake of Pb and Hg by infants through breastfeeding can be considered a health concern in Iran. It seems necessary to reduce the Pb exposure of pregnant and lactating women in Iran. However, more extensive studies are needed to clarify the toxic metals' exposure status of infants through breast milk in other parts of the country.
Keyphrases
- heavy metals
- risk assessment
- human health
- health risk
- health risk assessment
- fluorescent probe
- aqueous solution
- living cells
- sewage sludge
- public health
- healthcare
- pregnant women
- systematic review
- emergency department
- drinking water
- preterm infants
- mental health
- climate change
- type diabetes
- big data
- randomized controlled trial
- nk cells
- physical activity
- pregnancy outcomes
- study protocol
- polycystic ovary syndrome
- deep learning