Mercury, lead and cadmium in human milk in relation to diet, lifestyle habits and sociodemographic variables in Madrid (Spain)

Background

Although breastfeeding is the ideal way of nurturing infants, it can be a source of exposure to toxicants. This study reports the concentration of Hg, Pb and Cd in breast milk from a sample of women drawn from the general population of the Madrid Region, and explores the association between metal levels and socio-demographic factors, lifestyle habits, diet and environmental exposures, including tobacco smoke, exposure at home and occupational exposures.

Methods

Breast milk was obtained from 100 women (20 mL) at around the third week postpartum. Pb, Cd and Hg levels were determined using Atomic Absorption Spectrometry. Metal levels were log-transformed due to non-normal distribution. Their association with the variables collected by questionnaire was assessed using linear regression models. Separate models were fitted for Hg, Pb and Cd, using univariate linear regression in a first step. Secondly, multivariate linear regression models were adjusted introducing potential confounders specific for each metal. Finally, a test for trend was performed in order to evaluate possible dose-response relationships between metal levels and changes in variables categories.

Results

Geometric mean Hg, Pb and Cd content in milk were 0.53 μg L⁻¹, 15.56 μg L⁻¹, and 1.31 μg L⁻¹, respectively. Decreases in Hg levels in older women and in those with a previous history of pregnancies and lactations suggested clearance of this metal over lifetime, though differences were not statistically significant, probably due to limited sample size. Lead concentrations increased with greater exposure to motor vehicle traffic and higher potato consumption. Increased Cd levels were associated with type of lactation and tended to increase with tobacco smoking.

Conclusions

Surveillance for the presence of heavy metals in human milk is needed. Smoking and dietary habits are the main factors linked to heavy metal levels in breast milk. Our results reinforce the need to strengthen national food safety programs and to further promote avoidance of unhealthy behaviors such as smoking during pregnancy.     

The use of artificial neural networks to forecast biological atmospheric allergens or pathogens only as Alternaria spores

The monitoring of atmospheric Alternaria spores is of major importance due to their adverse effects on crops and their role as human allergens. Most species act as plant pathogens, prompting considerable economic losses worldwide on important crops such as potato, tomato or wheat. Fungal spores can also have serious detrimental effects on human health, triggering respiratory diseases and allergenic processes. The aim of this study was not only to examine the relationship between the atmospheric Alternaria spore content and the prevailing meteorological parameters, but also to predict the atmospheric Alternaria spore content in the Northwest Spain using a novel data analysis technique, ANNs (Artificial Neural Networks). A Hirst-type LANZONI VPPS 2000 volumetric 7-day recording sampler was used to collect the airborne spores from 1997 to 2008. Neural networks provided us with a good tool for forecasting Alternaria airborne spore concentration, and thus could help the automation of the prediction system in the aerobiological information diffusion to the population suffering from allergic problems or the prevention of considerable economic worldwide losses on important crops. Our proposed model would be applied to different geographical areas; nevertheless, the adjustment of the model, by using the available and adequate variables, would be realised in each case.     

Multilayer perceptron neural network for flow prediction

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.     

Newborns and low to moderate prenatal environmental lead exposure: might fathers be the key?

This study is part of the BioMadrid Project, a bio-monitoring study designed to assess pollutants in the environment surrounding children born in the Madrid region. Our aim in this report is to evaluate the association between prenatal lead exposure and fetal development using three biological samples (maternal and paternal blood lead at around 34 weeks of gestation as well as cord blood lead levels), three biomarkers of effect in cord blood peripheral lymphocytes (micronucleus in binucleated cells, nucleoplasmic bridges, and nuclear buds), and different anthropometrical characteristics at birth. Maternal and cord blood lead were not associated with newborn measurements or genotoxicity biomarkers. In contrast, increases in father blood lead were coupled with lower weight (mean difference (MD), ?110.8 g; 95 % confidence intervals (95%CI), ?235.6 to 6.00; p?<?0.10) and shorter abdominal (MD, ?0.81 cm; 95%CI, ?1.64 to 0.00; p?<?0.05) and cephalic (MD, ?0.32 cm; 95%CI, ?0.65 to 0.00; p?<?0.05) circumferences at birth as well as with the presence of nucleoplasmic bridges (odds ratio, 1.03; 95%CI, 1.00 to 1.06; p?<?0.05) and nuclear buds (odds ratio, 1.02; 95%CI, 0.99 to 1.04; p?<?0.10). These associations were mainly confined to female babies, in whom paternal lead was also inversely associated with length. Our results support the hypothesis that paternal lead exposure may be affecting the development of newborns.