- Research article
- Open Access
Maternal blood cadmium, lead and arsenic levels, nutrient combinations, and offspring birthweight
BMC Public Health volume 17, Article number: 354 (2017)
Cadmium (Cd), lead (Pb) and arsenic (As) are common environmental contaminants that have been associated with lower birthweight. Although some essential metals may mitigate exposure, data are inconsistent. This study sought to evaluate the relationship between toxic metals, nutrient combinations and birthweight among 275 mother-child pairs.
Non-essential metals, Cd, Pb, As, and essential metals, iron (Fe), zinc (Zn), selenium (Se), copper (Cu), calcium (Ca), magnesium (Mg), and manganese (Mn) were measured in maternal whole blood obtained during the first trimester using inductively coupled plasma mass spectrometry. Folate concentrations were measured by microbial assay. Birthweight was obtained from medical records. We used quantile regression to evaluate the association between toxic metals and nutrients due to their underlying wedge-shaped relationship. Ordinary linear regression was used to evaluate associations between birth weight and toxic metals.
After multivariate adjustment, the negative association between Pb or Cd and a combination of Fe, Se, Ca and folate was robust, persistent and dose-dependent (p < 0.05). However, a combination of Zn, Cu, Mn and Mg was positively associated with Pb and Cd levels. While prenatal blood Cd and Pb were also associated with lower birthweight. Fe, Se, Ca and folate did not modify these associations.
Small sample size and cross-sectional design notwithstanding, the robust and persistent negative associations between some, but not all, nutrient combinations with these ubiquitous environmental contaminants suggest that only some recommended nutrient combinations may mitigate toxic metal exposure in chronically exposed populations. Larger longitudinal studies are required to confirm these findings.
Trace metals including cadmium (Cd), lead (Pb) and arsenic (As) are common environmental contaminants. Main routes of human exposure are ingestion and inhalation of contaminated dust and food . These metals readily enter the food supply [2, 3], bioaccumulate, and target multiple organs. Exposure to these trace metals is implicated in cardiometabolic diseases, including cardiovascular diseases [4, 5], diabetes [4, 6], renal disorders [7, 8] and some types of cancer [9,10,11,12,13]. Some data suggest effects of these metals are sex- or race-specific [14, 15]. In both humans and experimental model systems, fetal exposure to these trace metals is associated with lower birthweight [16,17,18,19,20,21,22,23] and shorter birth length [17, 19, 24,25,26,27,28,29,30]. While non-specific, low birthweight is a consistent risk factor for obesity and cardiometabolic disease in adulthood [31,32,33]. Strategies to reduce or eliminate toxic metal exposure could have widespread implications for fetal health and adult chronic disease.
Dietary manipulation together with environmental management , are recommended strategies to mitigate exposure and its downstream health effects in chronically exposed populations. For example, iron (Fe) deficiency in humans has been linked to elevated levels of Cd in both blood and urine, independent of smoking, poverty, age, race, obesity and parity . Deficiencies in essential metals including Cu , Fe [37,38,39,40], manganese (Mn)  and magnesium (Mg) [42,43,44], and zinc (Zn) [45, 46] have been associated with increased risk of low birth weight and adiposity in some, but not all investigations [47, 48]. In animals, deficiencies in folate (a one-carbon cycle nutrient critical to the generation of S-adenosyl methionine, the universal methyl group donor) reduce As methylation and excretion . This animal data is bolstered by epidemiologic data showing that one-carbon cycle supplementation is associated with re-methylation of interspersed repeat elements  and lower As levels in populations chronically exposed to As .
Collectively, these data suggest that dietary repletion with essential metals may alleviate trace metal exposure and/or effects. However, associations are inconsistent and, to date, most investigations have only evaluated individual nutrients without examining their combinations or accounting for the non-linear relationship between toxic metals and nutrients . These analyses overcome somes of these limitations by:  evaluating the cross sectional relationship between Cd, Pb and As, and eight nutrients (e.g. Fe, selenium [Se], calcium [Ca], Mg, Mn, copper [Cu], Zn and folate), both independently and in combination; and  using quantile regression models to account for non-linear effects. We additionally examined whether maternal blood concentrations of either toxic metals or nutrient combinations are associated with offspring weight at birth.
Study participants were English or Spanish speaking pregnant women whom, during an 18 month period, between April 2009 and October 2011, were aged ≥18-years of age and attending a prenatal clinic serving Duke University and Durham Regional obstetric facilities . Of 2548 eligible women that were approached, 1700 (66.7%) consented. Women who declined study participation were more likely to be of Asian and Native American (p < 0.001) descent, but similar to enrolled women with respect to other characteristics. Cd, As and Pb were measured in whole peripheral blood of the first 310 women enrolled and the present analyses are limited to the 275 mother-infant pairs with complete exposure data for heavy metals, nutrients and birthweight. The distributions of covariates including maternal age at delivery, race/ethnicity, pre-pregnancy body mass index (BMI), education, smoking and gestational age at blood draw and at delivery were comparable in the 275 women and the larger cohort of 1700 (p values >0.05).
At enrollment, all participants completed a self- or interviewer-administered questionnaire soliciting information on demographic, reproductive history, lifestyle behaviors, and anthropometric characteristics. Maternal peripheral blood samples were collected at enrollment at median gestational age 12 weeks (Inter-quantile range 8 – 14 weeks) in 10 mL EDTA-treated vaccutainer tubes from which one mL of whole blood was removed for use in these studies and stored at −80 °C. Upon delivery, parturition data were abstracted from medical records.
Measurement of folate and trace metals
Maternal concentrations of folate, Cd, Pb, As, Fe, Zn, Se, Cu, Ca, Mg and Mn were measured in maternal whole blood. Folate concentrations were measured using a commercial kit, ID-Vit Folic acid (Immundiagnostic-ALPCO; Salem, NH) . Metal concentrations were measured as nanograms per gram (ng/g) using solution-based inductively coupled plasma mass spectrometry (ICP-MS) methods previously described [55,56,57]. All standards, including aliquots of the certified NIST 955c, and procedural blanks were prepared by the same process. Maternal metal concentrations were measured using a Perkin Elmer DRC II (Dynamic Reaction Cell) axial field ICP-MS at GeoMed Analtyical [55,56,57,58].
To clean sample lines and reduce memory effects, sample lines were sequentially washed with 18.2 MΩ cm resistance (by a Milli-Q water purification system, Millipore, Bedford, Mass., USA) water for 90 s and a 2% nitric acid solution for 120 s between analyses. To monitor and correct for instrumental and procedural backgrounds, procedural blanks were analyzed within each block of 10 samples. Calibration standards included aliquots of 18.2 MΩ cm resistance H2O, NIST 955c SRM, and NIST 955c SRM spiked with known quantities of each metal in a linear range from 0.025 to 10 ng/g. Standards were prepared from 1000 mg/L single element standards obtained from SCP Science, USA. Method detection limits (MDLs) were calculated according to the two-step approach using the t99SLLMV method (USEPA, 1993) at 99% CI (t = 3.71). To facilitate comparisons with prior studies, trace metal concentrations were converted from ng/g to μg/dl based on blood density of 1.035g/ml. The MDLs yielded values of 0.006, 0.005, and 0.071 μg/dL, for Cd, Pb, and As, respectively. Limits of detection (LOD) were 0.002, 0.002, and 0.022 μg/dL, for Cd, Pb and As, respectively, and limits of quantification (LOQ) (according to Long and Winefordner, 1983) were 0.0007, 0.0006, and 0.0073 μg/dL for Cd, Pb, and As, respectively. The number of samples below the LOD for Cd, Pb, and As were 2, 2, and 1, respectively.
Assessment of birthweight and covariates
Parturition data were abstracted from medical records by trained personnel after delivery. These data included birthweight (grams), gestational age at birth (weeks) and infant sex (male/female). Infant birthweight was normally distributed and analyzed as a continuous variable. The median values and range for Cd and Pb varied in strata of several covariates. These covariates were explored as potential confounders in the associations between toxic metals and nutrients, as well as birthweight. Covariates included maternal age at delivery (<30, 30–35, and >35 years), race/ethnicity (White, African American, Hispanic), pre-pregnancy BMI (<30/≥30 kg/m2), education (< high school, high school graduates/GED, and college graduates), smoking status (non-smoker/smoker), infant sex (male/female), and gestational age at birth (<37/≥37 weeks).
If we observed significant differences in toxic metal levels, we considered these covariates independently as potential confounders. Each individual covariate was retained in the model if its inclusion changed the association under investigation by 10% or more. For analysis of association between birthweight and Cd, Pb or As, a global test was performed to detect significant interaction between these metals and covariates. A priori, we considered maternal smoking and infant sex as potential effect modifiers of the relationship between toxic metals and maternal nutrients, as well as birthweight. We therefore examined associations within strata of these two variables.
Quantile regression models were used to evaluate associations between Cd, Pb and As, individually and eight nutrient concentrations (Fe, Zn, Se, Cu, Ca, Mg, Mn, and total folate) adjusting for maternal delivery age, race/ethnicity, pre-pregnancy BMI, education and smoking. We considered quantile regression because we observed a wedge-shaped relationship between maternal toxic metal and nutrient concentrations (see Additional file 1: Figure S1), which suggested that the magnitude of associations may differ by toxic metal quantile. The quantile regression  provided the slope of each nutrient at different quantile levels (τ) of the toxic metal (i.e., τ = 0.1 to 0.9 by increments of 0.05), adjusting for other nutrients and covariates. For each nutrient, a global test was performed to examine if the nutrient was associated with the toxic metal at any quantile level, and the significance level was Bonferroni-corrected for multiple nutrients and toxic metals, i.e., 0.05/(3 toxic metals ×8 nutrients) = 0.002.
Because nutrients are taken in mixtures and are correlated (see Additional file 2: Table S1), we also considered the aggregate effect of nutrients by computing two multi-nutrient indices: the negative-association nutrient (NAN) measure and the positive-association nutrient (PAN) measure. The NAN index was computed as the sum of the standardized value of Fe, Se, Ca, and folate, which appeared to be negatively associated with toxic metals in the joint analysis. The PAN index was computed as the sum of standardized values of Zn, Cu, Mg and Mn, which appeared to be positively associated with toxic metals in the joint analysis. Standardization before summing assured that the nutrients were pooled together on a comparable scale. For each toxic metal, quantile regressions were conducted treating the toxic metal as the response variable and the two nutrients indices and other covariates as explanatory variables. All results were also Bonferroni-corrected (0.05/(3 toxic metals ×2 nutrient indices) = 0.008) to account for multiple comparisons.
Ordinary linear regression analysis was used to evaluate the association between toxic metals and birthweight, adjusted for maternal age at delivery, race/ethnicity, education, smoking status, pre-pregnancy BMI, gestational age at birth and infant sex, as well as the association between the two nutrient indices (NAN and PAN) and birthweight. Due to the potential non-linear relationship between birthweight and toxic metals (i.e., Additional file 3: Figure S2) and between birthweight and nutrient indices (i.e., Additional file 4: Figure S3), we categorized the toxic metal values and nutrient indices into low (i.e., concentration below the 33rd percentile), moderate (between the 33rd and 67th percentiles) and high (i.e., above the 67th percentile). In addition, because the exposures of Cd and Pb in the NEST cohort are geographically clustered with co-exposure to Cd and Pb  (but not among other toxic metals), we modeled the co-exposure effect of Cd and Pb by including the interaction terms between Cd and Pb. We also considered the interactions between toxic metals and nutrient indices to assess if the metal effects on birthweight can be modified by nutrients. Cigarette smoking is a major source of numerous toxic metals including Cd and Pb, and sex-specific effects have been hypothesized previously. Therefore, associations between Cd, Pb or As in relation to birthweight were evaluated in all participants and in strata of prenatal exposure to infant sex and cigarette smoke adjusted by other covariates [30, 60] (a priori p = 0.05). Due to the small number of smokers (n = 39), we did not estimate associations in this stratum.
The distributions of individual toxic metals in strata of potential covariates are shown in Table 1. While there was some variation in the range of As by covariate, the median value remained comparable across covariates.
Association between toxic metals and individual nutrients
Quantile regression coefficient estimations (τ), along with their 95% simultaneous confidence bands  adjusted for other nutrients and maternal age, race/ethnicity, pre-pregnancy BMI, education, and smoking, are shown in Fig. 1. We found significant negative associations between Cd and Fe and folate concentrations, and positive associations with Cu and Mn. With the exception of Mn and folate, most regression coefficients were significant in the middle range of Cd. Similarly, negative associations were also found between Pb concentrations and Fe, Ca and folate, while associations with Zn, Cu and Mn were positive. In general, regression coefficients were significant in the upper quantiles of Pb for Fe, Cu and Ca. In contrast, coefficients for folate and Mn were significant in a limited range. For As, we found significant negative associations with Se and folate at only one quantile point, and significant positive associations with Zn and Cu. Coefficients of Se remained relatively constant across quantiles of As, while Zn, Cu and folate had slope estimates further away from zero in upper quantiles of As exposure.
Association between toxic metals and nutrient mixtures
The last column of Fig. 1 shows quantile regression for the sum of standardized negatively and positively associated indices, NAN and PAN, respectively, in relation to Cd, Pb and As. Cd levels were significantly associated with both nutrition indices (p-values <0.0002). In contrast to individual nutrients, where associations were observed for some range of quantiles (i.e. for Fe, τ = 0.55 , 0.7 ~ 0.85), associations with the nutrient index remained significant for most of the quantiles. Similarly, Pb levels were significantly associated with both NAN and PAN indices (p-values <0.0002), and the associations remained consistent for all quantiles of Pb. We also observed that As levels remained significantly negatively associated with the NAN index only in a limited range (τ = 0.1 ~ 0.15 , 0.4 ~ 0.75) and were no longer significant at the highest quintiles.
Association between toxic metals, nutrient mixtures and birthweight
Overall, medium level of As exposure were also associated with lower birthweight in all subjects (Table 2A), male infants (Table 2B) and non-smokers (Table 2D). In addition, high level of Cd was found associated with lower birthweight in male infants (Table 2B). In male-only analyses, we also observed significant interaction effects between Cd and PAN as well as between As and PAN. These interaction effects suggested that higher level of PAN might reduce the magnitude of birthweight loss in male infants compared to the baseline levels (i.e.,. low Cd and low PAN and low As and low PAN, respectively).
There were also significant metal and nutrient interacitons observed in all-subject analysis, including between Cd and PAN (where higher PAN levels reduced the magnitude of birthweight loss) as well as between As and NAN (for which, though high As and high NAN individually are positively associated with birthweight, the existence of both leads to birthweight loss).
We did not find significant co-exposure effects between Pb and Cd except in the female-only analysis (Table 2C), where we observed that female infants with moderate Cd and high Pb exposure tend to have higher birthweight compared to the baseline group of low Cd and low Pb.
We evaluated associations between Cd, Pb and As, and eight nutrients in pregnant women, and determined if exposure to these metals were associated with lower birthweight. We found that higher levels of a combination comprising Fe, Ca, Se and folate was robustly related to lower Pb and Cd, regardless of concentration of these toxic metals, whereas the negative relationships with single nutrients were within very narrow ranges of exposure. Surprisingly, higher levels of Cu, Zn, Mg and Mn were associated with higher levels of Pb and Cd. With the exception of recent data supporting positive associations between Mn and Cd , these findings are despite accumulated animal data and human single nutrient data to the contrary. Finally, exposure to Cd and As were negatively associated with birthweight as would be expected; but these associations were modified by select nutrient combinations. These findings are consistent with previous studies demonstrating that dietary repletion with Fe, Ca, Se and folate can mitigate exposure to Cd and Pb. However, our findings suggesting that Cu, Zn, Mg and Mn are positively associated with Cd and Pb levels contrast with previous studies that form the basis for existing intervention guidelines as they suggest these minerals may exacerbate exposure.
Our data showing negative, but weak and inconsistent associations between Pb, As or Cd and single nutrients are consistent with these divalent cations Fe2+ or Ca2+ competitively displacing Cd2+ in transmetallation reactions. In animals, Fe reduces intestinal absorption of Cd . These animal data are corroborated by human studies showing that Fe-deficiency is consistently associated with higher blood and urine Cd levels, independent of smoking, poverty, age, race, obesity and parity . Our data however contrast with in vitro evidence that show >50% lower Cd levels with Zn and Mn treatment, and negative relationships between Zn, Cu and Mg with Cd . These data provide early evidence that some essential metals, including Mn  may not mitigate exposure and effects of these metals. Specific mineral combinations that mitigate exposure may depend on the underlying nutritional status and may therefore vary by population exposed to Pb or Cd.
One-carbon cycle nutrients are critical in generating S-adenosyl methionine, key in the clearance of As . In mice  and humans [66,67,68,69], higher circulating one-carbon nutrients are associated with a lower body burden of inorganic As. Depletion of one-carbon cycle nutrients reduces As excretion and methylation [49, 70], whereas their repletion lowers blood As levels . In 6-year old As-exposed children, one-carbon cycle supplementation was associated with re-methylation of interspersed repeat elements  and lower As levels . Although fewer studies have been conducted on Pb or Cd, negative associations with folate or vitamin B12 have been reported . Our data provides evidence for negative associations between folate in combination with Fe, Ca and Se, and Cd or Pb levels.
Consistent with previous studies, we also found that prenatal exposure to Cd or As is associated with lower birthweight [16,17,18,19,20]. This adverse birth outcome increases the risk of rapid adiposity gain in young children; a consistent risk factor for cardiometabolic impairment in adulthood [72,73,74,75,76,77,78]. In adult cross-sectional studies, elevated Cd, Pb and As have been associated with cardiometabolic risk markers [79,80,81,82,83,84,85]. While metabolic syndrome is not clinically discernible in young children , individual cardiometabolic risk markers that include central adiposity, elevated systolic blood pressure and elevated levels of fasting insulin and/or glucose, triglycerides, cholesterol, accelerated adiposity, [87,88,89,90,91, 76,77,78, 92,93,94,95] sometimes without overt obesity , predict metabolic syndrome, atherosclerosis, diabetes and hypertension in adulthood [97,98,99,100,101,102,103,104]. While not confirmed by others , increased adiposity and higher insulin have been associated with deficiency of Cu , Fe [37,38,39,40], Mn , Mg [42,43,44] and Zn [45, 46]. Thus, the low birthweight that is associated with early exposure to these toxic metals supports the developmental origins of these cardiometabolic diseases, and may portend, and/or contribute to the increase in incidence of these diseases.
While these data suggest that some nutrients may mitigate toxic metal exposure and effects, mechanisms are poorly understood. In animal and in vitro models, ferritin reduces intestinal absorption of Cd  and Pb. Mn also shares transporters with Cd and Pb in vivo and in vitro. Cd induces oxidative stress via decreasing cellular antioxidant capacity, increases lipid peroxidation, and depletes glutathione and protein-bound sulfhydryl groups [105,106,107]. Because the target organs for these metals such as the kidney and liver also play a critical role in the maintenance of blood glucose levels [108,109,110,111], associations between these toxic metals and low birthweight that is often followed by accelerated adiposity gains and insulin resistance, are to be expected.
Our findings should be interpreted in the context of the study limitations. The relatively small sample size limited our ability to assess subgroup effects, and particularly higher order interactions (e.g. by combined maternal smoking, nutrient combinations and race/ethnicity), which may inform public health interventions. In addition, while we anticipate that some nutrient combinations may mitigate toxic metal exposure, cause-and-effect cannot be inferred in this cross-sectional study. Furthemore, we assessed maternal nutrient concentrations at a single time-point, yet these nutritional markers may vary throughout gestation, as the physiologic changes that occur during pregnancy may impact levels of toxic metals in maternal blood. For example, the increase in erythrocytes and plasma with advancing gestation may lower levels, presumably due to hemodilution, whereas essential elements may increase, in part due to increased supplementation. In support, a Canadian study reported that while Cd levels did not change, Pb levels decreased over the course of pregnancy, while Mn levels increased during the same period . Thus, given our specimens were collected during a short gestational window (median 12 weeks, IQR 8–14 weeks), the full effect of toxic metal and nutrients on birthweight may only be partially realized. However, sensitivity analyses showed that restricting analysis to women with gestational age > 14 weeks at blood draw did not alter our findings. Despite these limitations, our study has several strengths including the multiethnic composition of the cohort and our ability to examine multiple nutrients to mitigate exposure and effects very early in gestation, when many metabolic set points are established.
In summary, we provide early data suggesting that a combination of Fe, Ca, Se and folate is negatively associated with Cd and As exposure, while Cu, Zn, Mg and Mn may exacerbate exposure to these toxic compounds. We also confirmed negative associations between birthweight and toxic compounds, Cd and As. Larger studies are required to identify other nutrients, which in combination, may mitigate exposure to these ubiquitous toxic metals in exposed populations.
Body mass index
Inductively coupled plasma mass spectrometry
Limits of detection
Limits of quantification
King KE, Darrah TH, Money E, et al. Geographic clustering of elevated blood heavy metal levels in pregnant women. BMC Public Health. 2015;15:1035.
Satarug S, Moore MR. Adverse health effects of chronic exposure to low-level cadmium in foodstuffs and cigarette smoke. Environ Health Perspect. 2004;112:1099–103.
Satarug S, Moore MR. Emerging roles of cadmium and heme oxygenase in type-2 diabetes and cancer susceptibility. Tohoku J Exp Med. 2012;228:267–88.
Agarwal S, Zaman T, Tuzcu EM, Kapadia SR. Heavy metals and cardiovascular disease: results from the National Health and nutrition examination survey (NHANES) 1999–2006. Angiology. 2011;62:422–9.
Tellez-Plaza M, Guallar E, Howard BV, Navas-Acien A. Cadmium and cardiovascular risk. Epidemiology. 2013;24:784–5.
Chen L, Lei L, Jin T, Nordberg M, Nordberg GF. Plasma metallothionein antibody, urinary cadmium, and renal dysfunction in a Chinese type 2 diabetic population. Diabetes Care. 2006;29:2682–7.
Huang M, Choi SJ, Kim DW, et al. Evaluation of factors associated with cadmium exposure and kidney function in the general population. Environ Toxicol. 2013;28:563–70.
Liang Y, Lei L, Nilsson J, et al. Renal function after reduction in cadmium exposure: an 8-year follow-up of residents in cadmium-polluted areas. Environ Health Perspect. 2012;120:223–8.
Christensen BC, Marsit CJ. Epigenomics in environmental health. Front Genet. 2011;2:84.
Christensen BC, Marsit CJ, Houseman EA, et al. Differentiation of lung adenocarcinoma, pleural mesothelioma, and nonmalignant pulmonary tissues using DNA methylation profiles. Cancer Res. 2009;69:6315–21.
Gallagher CM, Chen JJ, Kovach JS. Environmental cadmium and breast cancer risk. Aging (Albany NY). 2010;2:804–14.
Benbrahim-Tallaa L, Tokar EJ, Diwan BA, Dill AL, Coppin JF, Waalkes MP. Cadmium malignantly transforms normal human breast epithelial cells into a basal-like phenotype. Environ Health Perspect. 2009;117:1847–52.
Tokar EJ, Benbrahim-Tallaa L, Waalkes MP. Metal ions in human cancer development. Met Ions Life Sci. 2011;8:375–401.
Ferraro PM, Sturniolo A, Naticchia A, D'Alonzo S, Gambaro G. Temporal trend of cadmium exposure in the United States population suggests gender specificities. Intern Med J. 2012;42:691–7.
Scinicariello F, Abadin HG, Murray HE. Association of low-level blood lead and blood pressure in NHANES 1999–2006. Environ Res. 2011;111:1249–57.
Nishijo M, Nakagawa H, Honda R, et al. Effects of maternal exposure to cadmium on pregnancy outcome and breast milk. Occup Environ Med. 2002;59:394–6. discussion 7
Nishijo M, Tawara K, Honda R, Nakagawa H, Tanebe K, Saito S. Relationship between newborn size and mother's blood cadmium levels, Toyama, Japan. Arch Environ Health. 2004;59:22–5.
Salpietro CD, Gangemi S, Minciullo PL, et al. Cadmium concentration in maternal and cord blood and infant birth weight: a study on healthy non-smoking women. J Perinat Med. 2002;30:395–9.
Ronco AM, Arguello G, Munoz L, Gras N, Llanos M. Metals content in placentas from moderate cigarette consumers: correlation with newborn birth weight. Biometals. 2005;18:233–41.
Fei DL, Koestler DC, Li Z, et al. Association between in Utero arsenic exposure, placental gene expression, and infant birth weight: a US birth cohort study. Environmental health: a global access science source. 2013;12:58.
Rodosthenous RS, Burris HH, Svensson K, et al. Prenatal lead exposure and fetal growth: smaller infants have heightened susceptibility. Environ Int. 2017;99:228–33.
Jedrychowski W, Perera F, Jankowski J, et al. Gender specific differences in neurodevelopmental effects of prenatal exposure to very low-lead levels: the prospective cohort study in three-year olds. Early Hum Dev. 2009;85:503–10.
Kaji M, Nishi Y. Lead and growth. Clinical pediatric endocrinology : case reports and clinical investigations: official journal of the Japanese Society for Pediatric Endocrinology. 2006;15:123–8.
Al-Saleh I, Shinwari N, Mashhour A, Rabah A. Birth outcome measures and maternal exposure to heavy metals (lead, cadmium and mercury) in Saudi Arabian population. Int J Hyg Environ Health. 2014;217:205–18.
Gundacker C, Frohlich S, Graf-Rohrmeister K, et al. Perinatal lead and mercury exposure in Austria. Sci Total Environ. 2010;408:5744–9.
Jones L, Parker JD, Mendola P. Blood lead and mercury levels in pregnant women in the United States, 2003–2008. NCHS data brief. 2010;52:1–8.
Koppen G, Den Hond E, Nelen V, et al. Organochlorine and heavy metals in newborns: results from the Flemish environment and health survey (FLEHS 2002–2006). Environ Int. 2009;35:1015–22.
Piasek M, Blanusa M, Kostial K, Laskey JW. Placental cadmium and progesterone concentrations in cigarette smokers. Reproductive toxicology (Elmsford, NY). 2001;15:673–81.
Shirai S, Suzuki Y, Yoshinaga J, Mizumoto Y. Maternal exposure to low-level heavy metals during pregnancy and birth size. J Environ Sci Health A Tox Hazard Subst Environ Eng. 2010;45:1468–74.
Faulk C, Barks A, Sanchez BN, et al. Perinatal lead (Pb) exposure results in sex-specific effects on food intake, fat, weight, and insulin response across the Murine life-course. PLoS One. 2014;9:e104273.
Ogden CL, Carroll MD, Flegal KM. High body mass index for age among US children and adolescents, 2003–2006. JAMA. 2008;299:2401–5.
Ong KK. Catch-up growth in small for gestational age babies: good or bad? Curr Opin Endocrinol Diabetes Obes. 2007;14:30–4.
Ong KK, Ahmed ML, Emmett PM, Preece MA, Dunger DB. Association between postnatal catch-up growth and obesity in childhood: prospective cohort study. BMJ. 2000;320:967–71.
Hu H, Kotha S, Brennan T. The role of nutrition in mitigating environmental insults: policy and ethical issues. Environ Health Perspect. 1995;103(Suppl 6):185–90.
Gallagher CM, Chen JJ, Kovach JS. The relationship between body iron stores and blood and urine cadmium concentrations in US never-smoking, non-pregnant women aged 20–49 years. Environ Res. 2011;111:702–7.
Wildman RE, Mao S. Tissue-specific alterations in lipoprotein lipase activity in copper-deficient rats. Biol Trace Elem Res. 2001;80:221–9.
Bourque SL, Komolova M, Nakatsu K, Adams MA. Long-term circulatory consequences of perinatal iron deficiency in male Wistar rats. Hypertension. 2008;51:154–9.
Komolova M, Bourque SL, Nakatsu K, Adams MA. Sedentariness and increased visceral adiposity in adult perinatally iron-deficient rats. International journal of obesity (2005). 2008;32:1441–4.
Menzie CM, Yanoff LB, Denkinger BI, et al. Obesity-related hypoferremia is not explained by differences in reported intake of heme and nonheme iron or intake of dietary factors that can affect iron absorption. J Am Diet Assoc. 2008;108:145–8.
Yanoff LB, Menzie CM, Denkinger B, et al. Inflammation and iron deficiency in the hypoferremia of obesity. International journal of obesity (2005). 2007;31:1412–9.
Ganeshan M, Sainath PB, Padmavathi IJ, et al. Maternal manganese restriction increases susceptibility to high-fat diet-induced dyslipidemia and altered adipose function in WNIN male rat offspring. Exp Diabetes Res. 2011;2011:486316.
Guerrero-Romero F, Bermudez-Pena C, Rodriguez-Moran M. Severe hypomagnesemia and low-grade inflammation in metabolic syndrome. Magnes Res. 2011;24:45–53.
Guerrero-Romero F, Rodriguez-Moran M. Low serum magnesium levels and metabolic syndrome. Acta Diabetol. 2002;39:209–13.
Guerrero-Romero F, Rodriguez-Moran M. Hypomagnesemia, oxidative stress, inflammation, and metabolic syndrome. Diabetes Metab Res Rev. 2006;22:471–6.
Jain CK, Gupta H, Chakrapani GJ. Enrichment and fractionation of heavy metals in bed sediments of river Narmada, India. Environ Monit Assess. 2008;141:35–47.
Jain CK, Rao VV, Prakash BA, Kumar KM, Yoshida M. Metal fractionation study on bed sediments of Hussainsagar Lake, Hyderabad, India. Environ Monit Assess. 2010;166:57–67.
Cheng HL, Griffin HJ, Bryant CE, Rooney KB, Steinbeck KS, O'Connor HT. Impact of diet and weight loss on iron and zinc status in overweight and obese young women. Asia Pac J Clin Nutr. 2013;22:574–82.
Mistry HD, Kurlak LO, Young SD, et al. Maternal selenium, copper and zinc concentrations in pregnancy associated with small-for-gestational-age infants. Matern Child Nutr. 2014;10:327–34.
Rossman TG, Klein CB. Genetic and epigenetic effects of environmental arsenicals. Metallomics. 2011;3:1135–41.
Hall MN, Liu X, Slavkovich V, et al. Folate, Cobalamin, Cysteine, Homocysteine, and arsenic metabolism among children in Bangladesh. Environ Health Perspect. 2009;117:825–31.
Gamble MV, Liu X, Slavkovich V, et al. Folic acid supplementation lowers blood arsenic. Am J Clin Nutr. 2007;86:1202–9.
Heindel JJ, Vandenberg LN. Developmental origins of health and disease: a paradigm for understanding disease cause and prevention. Curr Opin Pediatr. 2015;27:248–53.
Liu Y, Murphy SK, Murtha AP, et al. Depression in pregnancy, infant birth weight and DNA methylation of imprint regulatory elements. Epigenetics. 2012;7:735–46.
Horne DW, Patterson D. Lactobacillus casei microbiological assay of folic acid derivatives in 96-well microtiter plates. Clin Chem. 1988;34:2357–9.
Darrah TH, Prutsman-Pfeiffer JJ, Poreda RJ, Ellen Campbell M, Hauschka PV, Hannigan RE. Incorporation of excess gadolinium into human bone from medical contrast agents. Metallomics. 2009;1:479–88.
DeLoid G, Cohen JM, Darrah T, et al. Estimating the effective density of engineered nanomaterials for in vitro dosimetry. Nat Commun. 2014;5:3514.
McLaughlin MP, Darrah TH, Holland PL. Palladium (II) and platinum (II) bind strongly to an engineered blue copper protein. Inorg Chem. 2011;50:11294–6.
Sprauten M, Darrah TH, Peterson DR, et al. Impact of long-term serum platinum concentrations on neuro- and ototoxicity in Cisplatin-treated survivors of testicular cancer. J Clin Oncol. 2012;30:300–7.
Koenker R, Bassett G. Regression quantiles. Econometrica: journal of the Econometric Society. 1978;46:33–50.
Faulk C, Barks A, Liu K, Goodrich JM, Dolinoy DC. Early-life lead exposure results in dose- and sex-specific effects on weight and epigenetic gene regulation in weanling mice. Epigenomics. 2013;5:487–500.
Govarts E, Remy S, Bruckers L, et al. Combined effects of prenatal exposures to environmental chemicals on birth weight. Int J Environ Res Public Health. 2016;13
Djukic-Cosic D, Ninkovic M, Malicevic Z, Plamenac-Bulat Z, Matovic V. Effect of supplemental magnesium on the kidney levels of cadmium, zinc, and copper of mice exposed to toxic levels of cadmium. Biol Trace Elem Res. 2006;114:281–91.
Arbuckle TE, Liang CL, Morisset AS, et al. Maternal and fetal exposure to cadmium, lead, manganese and mercury: the MIREC study. Chemosphere. 2016;163:270–82.
McKay JA, Mathers JC. Diet induced epigenetic changes and their implications for health. Acta Physiol. 2011;202:103–18.
Tsang V, Fry RC, Niculescu MD, et al. The epigenetic effects of a high prenatal folate intake in male mouse fetuses exposed in utero to arsenic. Toxicol Appl Pharmacol. 2012;264:439–50.
Lambrou A, Baccarelli A, Wright RO, et al. Arsenic exposure and DNA methylation among elderly men. Epidemiology. 2012;23:668–76.
Gruber JF, Karagas MR, Gilbert-Diamond D, et al. Associations between toenail arsenic concentration and dietary factors in a New Hampshire population. Nutr J. 2012;11:45.
Deb D, Biswas A, Ghose A, Das A, Majumdar KK, Guha Mazumder DN. Nutritional deficiency and arsenical manifestations: a perspective study in an arsenic-endemic region of West Bengal, India. Public Health Nutr. 2013;16:1644–55.
Heck JE, Gamble MV, Chen Y, et al. Consumption of folate-related nutrients and metabolism of arsenic in Bangladesh. Am J Clin Nutr. 2007;85:1367–74.
Vahter M, Marafante E. Effects of low dietary intake of methionine, choline or proteins on the biotransformation of arsenite in the rabbit. Toxicol Lett. 1987;37:41–6.
Suarez-Ortegon MF, Mosquera M, Caicedo DM, De Plata CA, Mendez F. Nutrients intake as determinants of blood lead and cadmium levels in Colombian pregnant women. Am J Hum Biol. 2013;25:344–50.
Barker DJ. Developmental origins of adult health and disease. J Epidemiol Community Health. 2004;58:114–5.
Whincup PH, Kaye SJ, Owen CG, et al. Birth weight and risk of type 2 diabetes: a systematic review. JAMA. 2008;300:2886–97.
Ezzahir N, Alberti C, Deghmoun S, et al. Time course of catch-up in adiposity influences adult anthropometry in individuals who were born small for gestational age. Pediatr Res. 2005;58:243–7.
Meas T, Deghmoun S, Armoogum P, Alberti C, Levy-Marchal C. Consequences of being born small for gestational age on body composition: an 8-year follow-up study. J Clin Endocrinol Metab. 2008;93:3804–9.
Howe LD, Tilling K, Benfield L, et al. Changes in ponderal index and body mass index across childhood and their associations with fat mass and cardiovascular risk factors at age 15. PLoS One. 2010;5:e15186.
Anderson EL, Howe LD, Fraser A, et al. Weight trajectories through infancy and childhood and risk of non-alcoholic fatty liver disease in adolescence: the ALSPAC study. J Hepatol. 2014;61:626–32.
de Kroon ML, Renders CM, van Wouwe JP, van Buuren S, Hirasing RA. The Terneuzen birth cohort: BMI change between 2 and 6 years is most predictive of adult cardiometabolic risk. PLoS One. 2010;5:e13966.
Gallagher CM, Meliker JR. Blood and urine cadmium, blood pressure, and hypertension: a systematic review and meta-analysis. Environ Health Perspect. 2010;118:1676–84.
Wallia A, Allen NB, Badon S, El Muayed M. Association between urinary cadmium levels and prediabetes in the NHANES 2005–2010 population. Int J Hyg Environ Health. 2014;217:854–60.
Tellez-Plaza M, Jones MR, Dominguez-Lucas A, Guallar E, Navas-Acien A. Cadmium exposure and clinical cardiovascular disease: a systematic review. Current atherosclerosis reports. 2013;15:356.
Karim MR, Rahman M, Islam K, et al. Increases in oxidized low-density lipoprotein and other inflammatory and adhesion molecules with a concomitant decrease in high-density lipoprotein in the individuals exposed to arsenic in Bangladesh. Toxicological sciences: an official journal of the Society of Toxicology. 2013;135:17–25.
Mendez MA, González-Horta C, Sánchez-Ramírez B, Ballinas-Casarrubias L, Hernández Cerón R, Viniegra Morales D, Baeza Terrazas FA, Ishida MC, Gutiérrez-Torres DS, Saunders RJ, Drobná Z, Fry RC, Buse JB, Loomis D, García-Vargas GG, Del Razo LM, Stýblo M. Chronic exposure to arsenic and markers of cardiometabolic risk: a cross-sectional study in Chihuahua, Mexico. Environ Health Perspect 2016.
Kuo CC, Moon K, Thayer KA, Navas-Acien A. Environmental chemicals and type 2 diabetes: an updated systematic review of the epidemiologic evidence. Current diabetes reports. 2013;13:831–49.
Schober SE, Mirel LB, Graubard BI, Brody DJ, Flegal KM. Blood lead levels and death from all causes, cardiovascular disease, and cancer: results from the NHANES III mortality study. Environ Health Perspect. 2006;114:1538–41.
Daniels SR, Greer FR. Lipid screening and cardiovascular health in childhood. Pediatrics. 2008;122:198–208.
Chinapaw MJ, Altenburg TM, van Eijsden M, Gemke RJ, Vrijkotte TG. Screen time and cardiometabolic function in Dutch 5–6 year olds: cross-sectional analysis of the ABCD-study. BMC Public Health. 2014;14:933.
Falaschetti E, Hingorani AD, Jones A, et al. Adiposity and cardiovascular risk factors in a large contemporary population of pre-pubertal children. Eur Heart J. 2010;31:3063–72.
Toschke AM, Reinehr T. Different anthropometric index changes in relation to cardiovascular risk profile change. Clin Nutr. 2008;27:457–63.
Calarge CA, Xie D, Fiedorowicz JG, Burns TL, Haynes WG. Rate of weight gain and cardiometabolic abnormalities in children and adolescents. J Pediatr. 2012;161:1010–5.
Messiah SE, Arheart KL, Natale RA, Hlaing WM, Lipshultz SE, Miller TL. BMI, waist circumference, and selected cardiovascular disease risk factors among preschool-age children. Obesity. 2012;20:1942–9.
Eriksson JG, Kajantie E, Lampl M, Osmond C. Trajectories of body mass index among children who develop type 2 diabetes as adults. J Intern Med. 2015;
Huang RC, de Klerk NH, Smith A, et al. Lifecourse childhood adiposity trajectories associated with adolescent insulin resistance. Diabetes Care. 2011;34:1019–25.
Lawlor DA, Benfield L, Logue J, et al. Association between general and central adiposity in childhood, and change in these, with cardiovascular risk factors in adolescence: prospective cohort study. BMJ. 2010;341:c 6224.
Puder JJ, Schindler C, Zahner L, Kriemler S. Adiposity, fitness and metabolic risk in children: a cross-sectional and longitudinal study. International journal of pediatric obesity : IJPO: an official journal of the International Association for the Study of Obesity. 2011;6:e297–306.
Ong KK, Dunger DB. Birth weight, infant growth and insulin resistance. Eur J Endocrinol. 2004;151(Suppl 3):U131–9.
Juonala M, Viikari JS, Raitakari OT. Main findings from the prospective cardiovascular risk in young Finns study. Curr Opin Lipidol. 2013;24:57–64.
Laitinen TT, Pahkala K, Magnussen CG, et al. Ideal cardiovascular health in childhood and cardiometabolic outcomes in adulthood: the cardiovascular risk in young Finns study. Circulation. 2012;125:1971–8.
Berenson GS, Srnivasan SR. Cardiovascular risk factors in youth with implications for aging: the Bogalusa Heart study. Neurobiol Aging. 2005;26:303–7.
Chen W, Srinivasan SR, Li S, Xu J, Berenson GS. Metabolic syndrome variables at low levels in childhood are beneficially associated with adulthood cardiovascular risk: the Bogalusa Heart study. Diabetes Care. 2005;28:126–31.
Morrison JA, Glueck CJ, Wang P. Childhood risk factors predict cardiovascular disease, impaired fasting glucose plus type 2 diabetes mellitus, and high blood pressure 26 years later at a mean age of 38 years: the Princeton-lipid research clinics follow-up study. Metab Clin Exp. 2012;61:531–41.
Sun SS, Grave GD, Siervogel RM, Pickoff AA, Arslanian SS, Daniels SR. Systolic blood pressure in childhood predicts hypertension and metabolic syndrome later in life. Pediatrics. 2007;119:237–46.
Nguyen QM, Srinivasan SR, Xu JH, Chen W, Kieltyka L, Berenson GS. Utility of childhood glucose homeostasis variables in predicting adult diabetes and related cardiometabolic risk factors: the Bogalusa Heart study. Diabetes Care. 2010;33:670–5.
Nguyen QM, Srinivasan SR, Xu JH, Chen W, Berenson GS. Changes in risk variables of metabolic syndrome since childhood in pre-diabetic and type 2 diabetic subjects: the Bogalusa Heart study. Diabetes Care. 2008;31:2044–9.
Nigam D, Shukla GS, Agarwal AK. Glutathione depletion and oxidative damage in mitochondria following exposure to cadmium in rat liver and kidney. Toxicol Lett. 1999;106:151–7.
Jurczuk M, Brzoska MM, Moniuszko-Jakoniuk J, Galazyn-Sidorczuk M, Kulikowska-Karpinska E. Antioxidant enzymes activity and lipid peroxidation in liver and kidney of rats exposed to cadmium and ethanol. Food and chemical toxicology: an international journal published for the British Industrial Biological Research Association. 2004;42:429–38.
Li R, Yuan C, Dong C, Shuang S, Choi MM. In vivo antioxidative effect of isoquercitrin on cadmium-induced oxidative damage to mouse liver and kidney. Naunyn Schmiedeberg's Arch Pharmacol. 2011;383:437–45.
Gerich JE. Role of the kidney in normal glucose homeostasis and in the hyperglycaemia of diabetes mellitus: therapeutic implications. Diabetic medicine: a journal of the British Diabetic Association. 2010;27:136–42.
Meyer C, Dostou JM, Gerich JE. Role of the human kidney in glucose counterregulation. Diabetes. 1999;48:943–8.
Meyer C, Dostou JM, Welle SL, Gerich JE. Role of human liver, kidney, and skeletal muscle in postprandial glucose homeostasis. Am J Physiol Endocrinol Metab. 2002;282:E419–27.
DeFronzo RA, Davidson JA, Del Prato S. The role of the kidneys in glucose homeostasis: a new path towards normalizing glycaemia. Diabetes Obes Metab. 2012;14:5–14.
We thank participants of the NEST study project. We also acknowledge Stacy Murray, Kennetra Irby, Siobhan Greene and Anna Tsent for their recruiting efforts and Carole Grenier, Erin Erginer for specimen handling.
This work was supported in part by the generous donation from Howard and Julia Clark, and research grants from the National Institute of Health (Grant no. R01-ES016772, P30 ES025128) and National Cancer Institute (R25CA057726). The funders had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
Availability of data and materials
Data are not publically available but may be requested through the corresponding author.
CH, JYT, YL, LM developed the research question. TD, AV, RM, SM, CH provided resources and data to support these analyses. YL, JYT, AM performed the statistical analyses. LM, YL, CSH, JYT, MM, CH were responsible for data intrpretation. LM, YL drafted the manuscript. All authors read and approved the final manuscript.
The authors declare they have no competing interest.
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Ethics approval and consent to participate
The study protocol was approved by the Institutional Review Boards of Duke University, Durham Regional Hospital and North Carolina State University. All participants were informed of the study aims, benefits and potential limitations and provided written consent prior to participation.
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Dashed black line is the ordinary least square regression fitted line which is flat indicating weak association between logCd and Fe at mean level. Solid red, yellow and blue line are the quantile regression fitted line on quantile of logCd at 90th quantile 50th quantile and 10th quantile respectively. (PNG 61 kb)
Spearman correlation among all nutrients. (DOC 32 kb)
Boxplot of infant birthweights on different levels of toxic metals. Toxic metals are classified into 3 levels: Low (33.3rd quantile and below), Moderate (33.3rd to 66.7th quantile) and High (66.7th quantile above). The results suggest a potential non-linear relationship between birthweight and Cd as well as between birthweight and As. (PNG 28 kb)
Boxplot of infant birth weight on different levels of nutrients indices, i.e., NAN and PAN. Nutrients indices are classified into 3 levels: Low (33.3rd quantile and below), Moderate (33.3rd to 66.7th quantile) and High (66.7th quantile above). The plots suggest a potential non-linear relationship between birthweight and NAN as well as between birthweight and PAN. (PNG 24 kb)
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Luo, Y., McCullough, L.E., Tzeng, J. et al. Maternal blood cadmium, lead and arsenic levels, nutrient combinations, and offspring birthweight. BMC Public Health 17, 354 (2017). https://doi.org/10.1186/s12889-017-4225-8
- Toxic metals
- Dietary nutrients