Occurrence of aflatoxin M1 in human breast milk in Bangladesh.
Farjana IslamAporajita Das TrishaJaasia Momtahena HafsaAkibul HasanGisela H DegenNurshad AliPublished in: Mycotoxin research (2021)
Breast milk is the best, most complete form of nutrition for newborns and infants. However, human milk can contain aflatoxin M1 (AFM1) upon ingestion of dietary mycotoxin contaminants, namely, aflatoxin B1 (AFB1), by lactating mothers. AFB1 and its hydroxylated metabolite AFM1 are potent carcinogens and thus an important issue in food safety and public health. This study is the first to explore the presence of AFM1 in breast milk samples from Bangladesh and assess infant exposure to this toxin, as a consequence of maternal mycotoxin intake. A total of 62 breast milk samples were collected from nursing mothers in Sylhet region of Bangladesh. The milk samples were collected between October 2019 and March 2020 and analyzed by a sensitive enzyme-linked immunosorbent assay. AFM1 was detected in 51.6% of the breast milk samples (colostrum, transitional and mature milk), with a mean concentration of 4.42 ± 0.56 pg/mL, and in the range between LOD (4.0 pg/mL) and 6.66 pg/mL. The frequent detection of AFM1 in breast milk indicates widespread dietary exposure to mycotoxins in our cohort. The estimated average daily intake of AFM1 for all nursed infants was 0.49 ng/kg b.w./day. No significant correlations were observed between AFM1 levels in human milk and food items regularly consumed by nursing women. Overall, AFM1 levels in breast milk samples from the Sylhet region of Bangladesh are moderate, and lower than the permissible levels established for AFM1 in dairy milk or infant formulae (50 and 25 ng/kg, respectively). Yet, this first data for AFM1 breast milk contaminant levels just reflect the recent situation in one cohort, and monitoring should be continued.
Keyphrases
- atomic force microscopy
- high speed
- human milk
- low birth weight
- public health
- single molecule
- physical activity
- escherichia coli
- risk assessment
- healthcare
- preterm infants
- type diabetes
- endothelial cells
- quality improvement
- human health
- high throughput
- machine learning
- deep learning
- preterm birth
- single cell
- induced pluripotent stem cells
- pregnancy outcomes