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Prenatal metal exposures and kidney function in adolescence in Project Viva

Environmental Health volume 23, Article number: 94 (2024) Cite this article

Abstract

Background

The developing kidney is vulnerable to prenatal environmental factors such as metal exposure, potentially altering the risk of later-life kidney dysfunction. This study examines the relationship between prenatal metal exposures, individually and as mixtures, and adolescent kidney function in Project Viva, a prospective longitudinal birth cohort in Massachusetts, USA.

Methods

We used data on metals measured in blood during pregnancy including 15 in the first trimester and four in the second trimester. We calculated estimated glomerular filtration rate (eGFR) in adolescents (mean: 17.7 years) using cystatin C- (eGFRcys) and creatinine-based (eGFRcreat) equations for children. We used linear regression for single metal analyses, and Bayesian kernel machine regression and quantile-based g-computation for mixture analyses, adjusting for relevant covariates. To account for multiple comparisons in the single metal analyses, we applied the Holm-Bonferroni procedure to control the false discovery rate.

Results

This study included 371 participants with first trimester metals and adolescent eGFR, and 256 with second trimester metals. Each doubling in first trimester cadmium concentration was associated with lower adolescent eGFRcys (β:-1.51; 95% CI:-2.83, -0.18). Each doubling in first trimester chromium (β:-1.45; 95% CI:-2.71, -0.19), nickel (β:-1.91; 95% CI:-3.65, -0.16), and vanadium (β:-1.69; 95% CI:-3.21, -0.17) was associated with lower adolescent eGFRcreat. After adjusting for multiple comparisons, p-values for associations between adolescent eGFR and chromium, nickel, vanadium and cadmium did not meet the criteria for significance. Metal mixture analyses did not identify statistically significant associations with adolescent eGFR.

Conclusions

These findings have important implications for future studies investigating the potential mechanisms through which prenatal metal exposures affect long-term kidney health in children.

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Background

Exposure to metals and metalloids in utero and early life may impact children’s long-term health. Metal and metalloids (hereafter referred to as metals) are able to cross the placenta and reach the vulnerable developing organ systems of the fetus [1]. Fetal kidneys may be particularly vulnerable as they are highly vascularized organs [2]. The kidneys develop early in pregnancy, as early as five weeks gestation, and nephron development increases exponentially in the late second trimester and early third trimester [3]. Exposure to environmental toxicants, such as cadmium, lead and mercury, in utero may lead to structural and functional abnormalities in the developing kidney [4]. Additionally, because there is limited capacity for the kidney to regenerate, early life exposures to toxicants may have long-term consequences for kidney health, including a higher risk for chronic kidney disease (CKD) and hypertension [5, 6].

A limited number of epidemiological studies have examined metal exposure associations on child and adolescent kidney function, and even fewer examined associations of prenatal exposures specifically. Three studies examined the relationship of prenatal metal exposure to arsenic, cadmium and lead with kidney health parameters in childhood in the MINIMat cohort in Bangladesh [7,8,9]. Briefly, Hawkesworth et al. showed that arsenic exposure at 8 weeks of gestation was associated with lower estimated glomerular filtration rate (eGFR) at age 4.5y, although this was not confirmed in Akhtar et al.’s follow-up study in the same cohort. Additionally, Skröder et al. showed that blood lead levels were associated with a decrease of kidney volume in children at age 4.5y, with a stronger association in girls compared with boys. In the Programming Research in Obesity Growth, Environment and Social Stressors (PROGRESS) cohort in Mexico City, Saylor et al. reported that higher blood lead concentrations in the second and third trimesters were associated with lower eGFR in overweight children at age 9y, but the association was not observed in normal weight children [10].

Examining single metal exposures, however, may not be sufficient to understand environmental toxicant impacts on health outcomes. Individuals are often exposed to a complex mixture of metals which can act jointly to affect health, even when the individual metal components of the mixtures are present at low levels. To our knowledge, very few studies have examined the effects of metal mixtures on developing kidneys in children. In the PROGRESS cohort, prenatal exposure to metal mixtures assessed in deciduous teeth was associated with lower eGFR at ages 8-12y, driven by higher lithium and chromium concentrations between 8 and 17 weeks of gestation [11]. In the same cohort, higher concentrations of prenatal urine metal mixtures were associated with higher urine albumin and cystatin C ‒ biomarkers for subclinical kidney injury ‒ at ages 8-12y, driven mainly by higher concentrations of arsenic and cadmium [12]. However, these findings may not be generalizable to US populations, given that children in developing countries such as Bangladesh and Mexico are likely exposed to much higher levels of environmental toxicants than US children. Given the paucity of generalizable epidemiologic studies on prenatal metal exposure and kidney function in US pediatric populations, and even fewer studies with long-term follow-up of children into adolescence, more research is needed to better understand the extent to which prenatal exposure to metals, both individually and as mixtures, affects long-term kidney health in US children.

To address this research gap, we used data from a prospective longitudinal birth cohort recruited in eastern Massachusetts, Project Viva, to examine the relationship of prenatal exposure to metals with adolescent kidney function. We hypothesized that higher maternal concentrations of metals with established nephrotoxicity including arsenic, cadmium, chromium, cesium, lead and mercury, both individually and as mixtures, would be associated with a lower eGFR in adolescent offspring.

Methods

Study population

Project Viva is a prospective longitudinal birth cohort which enrolled between 1999 and 2002. Women were recruited during their first prenatal visit in the first trimester attending the Atrius Harvard Vanguard Medical Associates multispecialty group practice located in eastern Massachusetts. We have described the recruitment and inclusion and exclusion criteria previously [13]. Of 2,128 live births, we obtained blood samples from 1,423 mothers in the first trimester for analysis of metals and trace elements, 1,022 mothers in the second trimester, and 505 children at the adolescent visit for measurement of eGFR. Our sample for the current analysis included 371 mother-child pairs with complete data on first trimester metals and adolescent eGFR, and 313 with data on second trimester metals and adolescent eGFR (Figs. 1 and S1). Mothers provided written informed consent at recruitment and all follow-up visits; adolescents provided verbal assent (if < 18 years) or written informed consent (if > 18 years) at the outcome visit. The Institutional Review Board at Harvard Pilgrim Health Care Institute approved the protocols for this study.

Fig. 1
figure 1

Flow chart of inclusion for participants with first trimester metals concentrations

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Blood trace element concentrations in pregnancy

In this study, we used existing data on metals in pregnancy that were originally collected as part of different funded studies within Project Viva with specific objectives that directed the metals measured [14, 15]. The process for measuring blood trace elements has been previously described [16]. Trained research assistants collected blood samples from pregnant women in the first trimester (mean 10 gestational weeks). We measured metal concentrations as erythrocyte fraction (ng/g erythrocytes); erythrocytes were isolated by centrifuging blood samples at 2,000 rpm at 4ºC for 10 min and stored at -70ºC. We quantified concentrations for 18 metals including aluminum (Al), arsenic (As), barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), cesium (Cs), copper (Cu), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), antimony (Sb), selenium (Se), tin (Sn), vanadium (V), and zinc (Zn) using triple quadrupole inductively coupled plasma–mass spectrometry (ICP-QQQ) (Agilent 8800 ICP-QQQ), run in tandem mass spectrometry (MS/MS) mode using appropriate cell gases and internal standards.

We quantified blood erythrocyte concentration of mercury (Hg) using a Direct Mercury Analyzer 80 (Milestone Inc.). We also measured blood erythrocyte concentrations of Pb, Mn, Se, and Hg in second trimester (mean 28 gestational weeks) blood samples at the Trace Metals Laboratory at Harvard T.H. Chan School of Public Health in Boston, MA. These four metals were the only elements that were measured with samples collected during the second trimester. Methods for measuring second trimester metals have been previously described [17, 18]. For metal concentrations below the limit of detection (LOD), we assigned values as LOD/(√2). We selected metals with detection rates > 60% for this analysis; three metals (Co, Sb and Sn) in the first trimester did not meet this threshold and thus were not selected for analysis. We have described quality control and reliability of measurements in our previous publications [15, 17,18,19].

Adolescent kidney function

We assessed two biomarkers of kidney function, plasma creatinine and cystatin C, from fasting blood samples obtained in adolescence (mean age 17.7y). We measured plasma creatinine by an enzymatic method (Roche Cobas 6000 system, Roche Diagnostics) and plasma cystatin C using a particle-enhanced immunoturbidimetric assay (Roche Cobas 6000 system, Roche Diagnostics). Subsequently, we used two equations to estimate eGFR: (1) Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Creatinine Equation (eGFRcreat) and, (2) CKD-EPI Cystatin C Equation (eGFRcys). We followed the recommendation by the American Society of Nephrology and National Kidney Foundation and did not include race in the equations [20, 21]. The detailed equations are as follows:

$$\eqalign{& {\rm{ eGFRcreat }} \cdot = \cdot 142 \cdot * \cdot \min {({\rm{cr}}/\kappa , \cdot 1)^{\alpha}} * \cr & \max {({\rm{cr}}/\kappa , \cdot 1)^{ - 1.20}}\,* \cdot {0.993^{{\rm{age }}}} \cdot * \cdot {1.012^{{\rm{female }}}} \cr}$$

where cr = plasma creatinine, κ = 0.7 for females and 0.9 for males, α= -0.241 for females and -0.302 for males, min = minimum of cr/ κ or 1, and max = maximum of cr/κ or 1, female = 1 for females and 0 for males.

$$\eqalign{& {\rm{eGFRcys }}\cdot = \cdot 133 \cdot * \cdot \min {({\rm{ cys }}/0.8, \cdot 1)^{ - 0.499 }} * \cr & \max {({\rm{ cys }}/0.8, \cdot 1)^{ - 1.328 }} * \cdot {0.996^{{\rm{age }} }} * \cdot {0.969^{{\rm{female }}}} \cr}$$

where cys = plasma cystatin C, min = minimum of cr/0.8 or 1, and max = the maximum of cr/0.8 or 1, female = 1 for females and 0 for males. An eGFR of ≥ 90 mL/min/1.73m2 is considered normal kidney function [22]. We used eGFR calculated with creatinine and cystatin C to allow for a more comprehensive assessment of kidney function as both measurements can be influenced by non-kidney factors such as body composition, physical activity, and the presence of other chronic conditions [23].

Covariates

Mothers reported their age, race and ethnicity, highest education, annual household income, prenatal smoking status, pre-pregnancy weight, and height via questionnaires and interviews at recruitment. We asked mothers: “Which of the following best describes your race or ethnicity?” Mothers had a choice of ≥ 1 of the following racial and ethnic groups: Hispanic or Latino/a, White or Caucasian, Black or African American, Asian or Pacific Islander, American Indian or Alaskan Native, and Other (please specify). Because of the small sample size, we combined those whose race was American Indian or Alaskan Native, Other, or more than 1 race or ethnicity into a single category of “Other or more than 1 race or ethnicity”. We included the covariate race and ethnicity as a proxy for shared differences in unmeasured variables such as experiencing racism and social marginalization [24, 25]. We categorized education level as no college degree or college degree and above, household income as ≤$70,000/year or >$70,000/year, and smoking status as never, former, or smoked during pregnancy. We calculated pre-pregnancy BMI as self-reported pre-pregnancy weight in kilograms divided by height in square meters (kg/m2). We assessed maternal diet during the first and second trimester of pregnancy using validated, self-administered, semi-quantitative food frequency questionnaires and calculated the Alternate Healthy Eating Index for Pregnancy (AHEI-P) [26], a dietary index that measures adherence to healthy eating patterns based on specific food and nutrient intake criteria. We obtained clinical hematocrit values from medical records during pregnancy, closest to the blood draw date for metals concentrations, creatinine and cystatin C. We used hematocrit levels to account for blood level of certain metals often associated with physiological increases in body fluid during pregnancy. We selected these covariates a priori based on prior literature that examined the relationship between maternal metal exposure and child kidney function as well as a directed acyclic graph [6, 27, 28] (Figure S2); specifically, these covariates are antecedent variables and related to both the exposure and outcome and thus are likely confounders in the relationship between prenatal metal exposure and adolescent kidney function.

Statistical analysis

We first log2-transformed prenatal metal concentrations to satisfy model assumptions as the distribution of the metal concentration was skewed. We analyzed correlations between each metal using Pearson’s correlation. Descriptive statistics for participant characteristics are presented using medians and interquartile ranges (IQRs) for continuous variables and frequencies with proportions for categorical variables. We calculated the medians and interquartile range of metal concentrations. We used multivariable linear regression models to assess the relationship between each individual metal and the two eGFR variables, both unadjusted and adjusted for covariates described above. We used the Holm-Bonferroni procedure to adjust for multiple comparisons.

We evaluated the joint effect of the metal mixture on kidney health using two different but complementary mixture methods. First, we employed Bayesian Kernel Machine Regression (BKMR) to assess potential interactions between multiple metals and nonlinear relationships between metal mixture components and the outcomes [29]. Three separate sets of metal mixtures were assessed in the mixture analyses, including all metals, metals with known kidney toxicity only (As, Cd, Cr, Cs, Pb, Hg) [12, 30,31,32], and metals with no known kidney toxicity only (Al, Ba, Cu, Mg, Mn, Mo, Ni, Se, V, Zn). Metals not included in the mixtures of the latter two models were not adjusted for as covariates. Our BKMR models were specified to include 50,000 Markov chain Monte Carlo (MCMC) iterations using the Gaussian kernel.

Second, informed by the results from BKMR regarding metal-metal interactions and non-linearity, we employed quantile-based g-computation to estimate the joint association of metal mixtures with eGFR. This method relaxes the directional homogeneity assumption and allows for individual components in the mixture to contribute either a positive or a negative weight to a mixture index and estimates the effect of the mixture index on the outcome [33]. We specified 10 quantiles and used 50,000 bootstraps to estimate the joint effect on outcome per decile increase in the metal mixture while adjusting for covariates mentioned in the previous section. We used 50,000 iterations in accordance with best practices and recommendations from prior literature [29]. We analyzed convergence diagnostics to determine if additional iterations were required.

We performed statistical analyses with SAS (version 9.3) and R software (version 4.2.1, R Foundation for Statistical Computing) and used bkmrhat (version 1.1.3) package for BKMR and qgcomp (version 2.9.0) package for quantile-based g-computation. When interpreting findings, we focused on the direction, strength, and precision (i.e., 95% CI) of the estimates. We defined statistical significance as α < 0.05.

Results

Sample characteristics

Characteristics of mother and child pairs are provided in Table 1. The mean (SD) age of mothers at enrollment was 32.9y (4.7) and pre-pregnancy BMI was 24.8 (5.1) kg/m2. Most mothers were non-Hispanic White (72%), never smoked (73%), had a college degree (75%) and an annual household income higher than $70,000 (67%), and 46% were nulliparous. Slightly more than half the children were female (54%). The mean (SD) gestational age at the first trimester blood draw was 10.0 (2.2) weeks, while the mean (SD) age of the child at the adolescent visit was 17.7y (0.7). The mean (SD) eGFRcys and eGFRcreat was 118.3 mL/min/1.73m2 (13.0) and 121.4 mL/min/1.73m2 (12.6), respectively. eGFRcys and eGFRcreat were weakly correlated (r = 0.26, p < 0.0001).

Table 1 Participant characteristics with metals measured in first trimester (n = 371)
Full size table

The distributions of the first trimester metal concentrations and reference ranges for metals in red blood cells are provided in Table 2. The 75th percentile of participant’s metal concentrations were lower for all metals when compared to available reference ranges. Four pairs of metals including Cr + V, Mo + V, and Mg + Se were strongly correlated (r ≥ 0.5). Additionally, seven pairs of metals including Cr + Mo, Mo + Ni, Cr + Ni, Ni + V, Mg + Zn, Se + Z, and As + Hg were moderately correlated (0.3 ≤ r < 0.5). Table S1 provides all Pearson correlation coefficients.

Table 2 Distributions of prenatal metal concentrations in first trimester erythrocyte samples in ng/g (n = 371)
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Individual metals and eGFR

After adjusting for covariates, each doubling of first trimester Cd concentration was associated with lower eGFRcys (β: -1.51 mL/min/1.73m2; 95% CI: -2.83, -0.18) (Fig. 2). Additionally, in adjusted models, each doubling in Cr (β: -1.45 mL/min/1.73m2; 95% CI: -2.71, -0.19), Ni (β -1.91: mL/min/1.73m2; 95% CI: -3.65, -0.16), and V (β: -1.69 mL/min/1.73m2; 95% CI: -3.21, -0.17) were associated with lower eGFRcreat (Fig. 3). All other first trimester metal concentrations were not significantly associated with adolescent eGFR (Figs. 2 and 3). Metal concentrations at the second trimester were not significantly associated with eGFRcys or eGFRcreat in adolescence (Figs. S3 and S4). Sample sizes for metals in the second trimester was smaller (n = 256) which may have reduced the power to observe associations. Using the Holm-Bonferroni procedure to adjust for multiple comparisons, the associations between eGFR and individual metals did not meet the criteria for significance.

Fig. 2
figure 2

Forest plot of regression beta estimates for first trimester metal concentrations vs. adolescent eGFRcys. Regression models were adjusted for covariates including maternal age, pre-pregnancy BMI, race and ethnicity, education level, household income, parity, pregnancy smoking status, hematocrit level, gestational age at blood draw, and AHEI diet score

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Fig. 3
figure 3

Forest plot of regression beta estimates for first trimester metal concentrations vs. adolescent eGFRcreat. Regression models were adjusted for covariates including maternal age, pre-pregnancy BMI, race and ethnicity, education level, household income, parity, pregnancy smoking status, hematocrit level, gestational age at blood draw, and AHEI diet score

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Metal mixtures and adolescent eGFR

Results from BKMR did not reveal any significant interaction between metals or nonlinear effects (Figs. S5 and S6), thus we did not include interaction or non-linear terms in the modeling with quantile based g-computation. The overall joint effect on adolescent eGFR estimated by quantile-based g-computation for all three sets of metal mixtures examined were null. While not statistically significant, higher levels of metal mixtures were generally associated with lower adolescent eGFR (Table S2).

Discussion

In this cohort study, we identified associations of prenatal exposures to specific individual metals (Cd, Cr, Ni, and V) with lower adolescent kidney function, although these relationships were no longer significant after adjusting for multiple comparisons. Exposure to higher concentrations of Cd, an established nephrotoxicant, during the first trimester of pregnancy was associated with lower adolescent kidney function. Relatedly, smoking is a major source of cadmium for individuals who smoke, and in our sample, 19% of participants were former smokers and 9% had smoked during pregnancy. Future analyses should examine the relationship between maternal smoking, prenatal cadmium concentrations and adolescent kidney function [34]. Cr and Ni concentrations are associated with cooking utensil material, second-hand smoke exposure and cosmetic use in pregnant women [35]. V is present in fossil fuels and is released into the atmosphere when those fuels are burned [36]. More research is needed to understand the sources of exposure to these metals in pregnancy in order to develop strategies for prevention.

No metals in the second trimester were associated with adolescent kidney function. Results from mixtures analyses also identified no significant associations with adolescent kidney function. Participants’ erythrocyte concentrations at the 75th percentile were within reference ranges, where available. It is important to evaluate the association of metal concentrations with health outcomes at or below reference levels to identify possible effects even at low levels. For example, at least 75% of the Viva population had prenatal V concentrations within the reference range, but still V was found to have an association with adolescent kidney function.

Our results, which identified associations of Cd, Cr, Ni and V with adolescent kidney function, complement prior studies from both developed and developing countries, although the matrices and timepoints in which metals were measured have varied. For example, in the PROGRESS cohort, higher prenatal maternal urinary Cd concentration was associated with altered urinary kidney injury biomarkers (alpha-1-microglobulin, clusterin, and albumin) in children ages 8-12y [12]. In the same cohort, higher child dietary Cd at age 1y was associated with lower eGFR at age 9y [37]. In Canada, a small cross-sectional study (n = 36) found elevated plasma levels of V and Cr in pediatric patients ages 4-18y with clinical CKD (i.e., eGFR < 30 mL/min/1.73 m2) [38]. Another small cross-sectional study (n = 83) in Mexico reported a dose-dependent association between increasing tertiles of urine Cr and higher levels of kidney injury molecule-1 [39]. Studies examining associations of Ni exposure with kidney outcomes, however, have mostly included adult populations and we found no prior studies that assessed associations with children’s kidney function. For example, a case-control study in adults aged 20-59y in Nicaragua (n = 54) showed that higher urine Ni concentrations were associated with lower eGFR [40]. In the US National Health and Nutrition Examination Survey (n = 1588), Nan et al. [41] showed that higher urinary Ni concentrations were associated with higher odds of impaired kidney function (defined as eGFR ≤ 60 mL/min/1.73 m2 or urinary albumin-creatinine ratio ≥ 30 mg/g) in adults. We did not observe associations between As, Pb and Hg, which are established nephrotoxicants. Altogether, our findings contribute to the small body of evidence that has examined the relationship between early-life exposure to metals and subsequent kidney function in children.

There are direct mechanisms by which Cd or Cr may affect kidney development and function. Gestational Cd exposure in pregnant rats led to structural damage in fetal kidneys as well as increased levels of kidney injury biomarkers in amniotic fluid, such as albumin, vascular endothelial growth factor, and metalloproteinases-1 which help maintain blood volume and pressure, promote blood vessel formation, and regulate tissue remodeling [42]. In mature kidneys, Cd exposure typically leads to an upregulation of metallothionein production in the liver and kidneys, which is a protective response to limit toxicity from free Cd. However, once the metallothionein-producing capacity of proximal kidney tubular cells is exhausted, progressive tubular cell damage occurs as the intracellular levels of Cd increase [43]. Cd can also lead to production of autoantibodies to metallothionein, which may be tubulo-toxic and interfere with Cd detoxification [44]. A study in pregnant rats exposed to Cr as potassium dichromate (K2Cr2O7) demonstrated that the exposed 14-day-old pups had lower creatinine clearance, an indicator of glomerular dysfunction, an increase in malondialdehyde, nitric oxide, glutathione and non-protein sulfhydryl levels, which are markers of oxidative stress and lipid peroxidation, and an increase in lactate dehydrogenase activity, a marker of kidney cellular damage [45]. While evidence showed pregnant dams were capable of mounting a protective response to oxidative stress, antioxidant enzymatic activities in the kidneys of the exposed pups were decreased suggesting their increased vulnerability to tissue damage. Another study in adult rats demonstrated intraglomerular hemorrhage, tubular dilatation, and localized necrosis of epithelial cells in proximal tubules after high-dose exposure to hexavalent chromium [Cr(VI)] [46]. In mature kidneys, hexavalent chromium [Cr(VI)]-induced cytotoxicity, DNA damage, and oxidative stress have also been observed in animal kidneys [47]; therefore, Cr(VI) might also induce human nephrotoxicity [48]. While the effects of V on developing kidneys is not well studied, exposure of pregnant mice to V was associated with restricted fetal growth and developmental abnormalities in skeletal structures [49]. A study of postnatal exposure to V in nursing mouse pups showed that low dose V caused mild tubular epithelial cell shedding, while high dose V was associated with renal glomerular atrophy, enlarged tubular lumens, and degenerated epithelial cells in the renal medulla [50]. The mechanism of Ni nephrotoxicity remains unknown; however, Ni has been shown to cause tissue injury through cytotoxicity, indirect damage, and immune dysregulation, processes which are known to accelerate and increase risk of end-stage kidney disease [31, 51]. While much of the research of the effects of metals on kidney health is done in adults, glomerular development begins around 9 weeks gestation and are thus susceptible to maternal exposures during key periods of fetal kidney development [3]. More research is thus needed to understand the effects and mechanisms of prenatal metal exposures on fetal kidney development in addition to the more prevalent research on the effects in mature kidneys.

We examined both creatinine- (eGFRcreat) and cystatin C-derived (eGFRcys) estimates of kidney function, and our results highlight the known differences between these measures [52], as associations with individual metals were inconsistent across the two models. A low-to-moderate correlation or agreement between creatinine- and cystatin C-derived eGFR is common among population-based studies and may be due to differences in population characteristics beyond kidney function that affect plasma creatinine and cystatin C levels [53,54,55]. In particular, plasma creatinine levels are affected by muscle mass, age, sex, and protein consumption, while cystatin C may be affected by adiposity, inflammation, thyroid levels, and corticosteroid use – all factors that may vary considerably with study population demographics and for which normative data among diverse, healthy adolescent populations are lacking. While cystatin C-based eGFR equations have been favored in the recent literature, creatinine-based eGFR is traditionally used because of lower assay costs, and increased clinician familiarity. The difference in results between eGFRcys and eGFRcreat models highlights the need for more research on these biomarkers and novel alternatives in diverse healthy adolescent populations. The variation in metal associations across the two eGFR models also highlights the importance of using multiple biomarkers to assess nephrotoxic effects and suggests potential confounding from non-kidney factors. More research is needed to explore whether these differences reflect distinct pathways through which metals affect kidney function, as creatinine and cystatin C capture different aspects of kidney health.

Our study had some limitations. First, we measured total As, Cr, and Hg rather than speciated forms and did not quantify inorganic As, hexavalent Cr, and methyl (organic or inorganic) Hg, for which adverse health effects may be species dependent. There is evidence from animal models that the nephrotoxic form of Cr(VI) that is not reduced to the inert trivalent form in plasma may be taken up by erythrocytes [57]. As is also rapidly metabolized in blood, therefore our lack of findings with As may be more indicative of biomarker limitations than a true null association [27]. Further, given the modest correlation between As and Hg, it is possible that As in this study may reflect seafood arsenicals which are largely considered non-toxic, rather than the more toxic inorganic arsenic species. Additionally, we examined metals in erythrocytes, which might not accurately reflect long-term exposure given that the average half-life of erythrocytes is about 3 months. However, this approach may wield advantages over metal assessment in urine, which can be affected by changes in excretion and hydration status and also reflects shorter-term exposures when compared with erythrocyte metal concentrations. Second, we assessed kidney function as plasma creatinine- or cystatin C-derived eGFR and did not ascertain clinical CKD status. While measured glomerular filtration rate (mGFR) via iohexol clearance is regarded as the gold standard for evaluating kidney function in clinical studies, in the setting of large population-based studies, eGFR calculated from stable creatinine or cystatin C values has been shown to be a viable alternative [56]. Third, differences between participants included and excluded from the analytic sample might conceivably have led to selection bias. However, our analyses adjusted for sociodemographic factors related to selection [58], thus minimizing selection bias. Fourth, we were only able to measure concentrations for the full set of metals in the first trimester, not the second. Although we have no clear biological explanation as to why associations with adolescent kidney function were seen with only the first, but not second trimester exposure to metals, it remains unclear whether our findings for metal exposure in the first trimester can be extended to the second trimester. Future studies are warranted to examine whether exposure to metals at other life stages, such as childhood or adolescence, may contribute to poorer kidney function. Finally, our findings may also not be generalizable to populations from different settings, because all participants were recruited from eastern Massachusetts and had access to health care.

Despite these limitations, our study had many strengths. We included a suite of individual trace metals which have not been well-studied previously with respect to kidney function, particularly among U.S. populations. In addition to individual metals, we also evaluated prenatal metal mixtures using methods which addresses some of the shortcomings of traditional individual trace element analysis, such as multiple comparisons, violation of linearity, and model misspecification [29, 33]. Further, prior studies have largely analyzed these associations cross-sectionally which subjects the findings to reverse causality. The prospective study design of Project Viva, however, reduces this risk. The comprehensive data collection within Project Viva also allowed us to account for important confounders such as diet and hematocrit level. Lastly, we were able to use two eGFR calculations which highlighted important differences in associations with individual metals, which requires further research.

Conclusions

Prenatal exposure to certain individual metals in the first (but not the second) trimester, even at low circulating levels, was associated with lower kidney function in adolescence, although these results did not meet the criteria for significance after correcting for multiple comparisons. We did not observe an association between prenatal metal mixture concentrations and adolescent kidney function. As children with kidney dysfunction face a disproportionate burden of morbidity and mortality [59], it is imperative that efforts focus on the promotion and preservation of kidney health prior to development of risk factors for CKD. Our differential findings for the associations of individual metals and eGFR calculated with cystatin C and creatinine warrants additional studies to better understand these biomarkers, their confounding with other factors, and the ways in which each are differentially affected by metals.

Data availability

The data underlying this article are available on reasonable request to the corresponding author and appropriate approval from the Project Viva team and Institutional Review Board.

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Acknowledgements

We thank the Project Viva participants and staff.

Funding

This work was supported by the US National Institutes of Health grants R00ES027508, R01HD034568, UH3OD023286, R24ES030894, R01ES031259, and R01ES033466. The Children’s Health Exposure Analysis Resource (CHEAR) funded the measurement of elements (CHEAR award #2017–1740) supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number (U2CES026561) and was carried out at the Mount Sinai CHEAR Network Laboratory with data processed by the CHEAR Data Center (U2CES026555).

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Author notes

  1. Izzuddin M. Aris and Alison P. Sanders contributed equally to this work.

Authors and Affiliations

  1. Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Natalie F. Price & Alison P. Sanders

  2. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA

    Pi-I D. Lin, Sheryl L. Rifas-Shiman, Marie-France Hivert, Emily Oken & Izzuddin M. Aris

  3. Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA

    Andres Cardenas

  4. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA

    Ami R. Zota

  5. Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA

    Marie-France Hivert

  6. Environmental and Occupational Health, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Alison P. Sanders

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  1. Natalie F. Price
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Contributions

N.F.P: Investigation, Writing- Original draft preparation, Writing - Review & Editing; P.D.L.: Investigation, Methodology, Software, Formal Analysis, Writing- Original draft preparation; A.C.: Writing - Review & Editing, Funding acquisition; S.R.S: Software, Formal Analysis, Data Curation, Writing - Review & Editing; A.R.Z.: Writing - Review & Editing; M.H: Writing - Review & Editing, Project administration, Funding acquisition; E.O.: Writing - Review & Editing, Funding acquisition; I.M.A: Conceptualization, Writing- Original draft preparation, Writing - Review & Editing, Supervision, Methodology; A.P.S.: Conceptualization, Methodology, Writing- Original draft preparation, Writing - Review & Editing, Supervision, Funding acquisition.

Corresponding author

Correspondence to Natalie F. Price.

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Ethics approval and consent to participate

The Harvard Pilgrim Health Care Institutional Review Board approved all study protocols in line with ethical standards established by the Declaration of Helsinki. We obtained written informed consent from the mothers and beginning in mid-childhood verbal assent from the child through age 18 years, after which we obtain written informed consent from offspring themselves.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Price, N.F., Lin, PI.D., Cardenas, A. et al. Prenatal metal exposures and kidney function in adolescence in Project Viva. Environ Health 23, 94 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01135-6

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Keywords

Environmental Health

ISSN: 1476-069X