Skip to main content
NCBI home page
Toxicological Sciences logoLink to Toxicological Sciences
. 2023 May 27;194(2):226–234. doi: 10.1093/toxsci/kfad054

Refining risk estimates for lead in drinking water based on the impact of genetics and diet on blood lead levels using the Collaborative Cross mouse population

Danila Cuomo 1, Megan Nitcher 2, Estefania Barba 3, Andrew P Feinberg 4,5,6, Ivan Rusyn 7, Weihsueh A Chiu 8, David W Threadgill 9,10,✉
PMCID: PMC10375319  PMID: 37243727

Abstract

Blood lead (Pb) level (BLL) is a commonly used biomarker to evaluate associations with health effects. However, interventions to reduce the adverse effects of Pb require relating BLL to external exposure. Moreover, risk mitigation actions need to ensure protection of more susceptible individuals with a greater tendency to accumulate Pb. Because little data is available to quantify inter-individual variability in biokinetics of Pb, we investigated the influence of genetics and diet on BLL in the genetically diverse Collaborative Cross (CC) mouse population. Adult female mice from 49 CC strains received either a standard mouse chow or a chow mimicking the American diet while being provided water ad libitum with 1000 ppm Pb for 4 weeks. In both arms of the study, inter-strain variability was observed; however, in American diet-fed animals, the BLL was greater and more variable. Importantly, the degree of variation in BLL among strains on the American diet was greater (2.3) than the default variability estimate (1.6) used in setting the regulatory standards. Genetic analysis identified suggestive diet-associated haplotypes that were associated with variation in BLL, largely contributed by the PWK/PhJ strain. This study quantified the variation in BLL that is due to genetic background, diet, and their interactions, and observed that it may be greater than that assumed for current regulatory standards for Pb in drinking water. Moreover, this work highlights the need of characterizing inter-individual variation in BLL to ensure adequate public health interventions aimed at reducing human health risks from Pb.

Keywords: BLL, genetics, diet, risk assessment, Collaborative Cross

Lead (Pb) is a naturally occurring metal that is extensively mined and has been used across a range of industries, resulting in extensive environmental re-distribution and contamination of water and soil leading in considerable human exposure. Common sources of environmental contamination by Pb are from mining, smelting, manufacturing, and use in a variety of consumer products (Jarup, 2003). Most of human exposure to Pb occurs through inhalation or ingestion. The former is thought to be the major route for workers in Pb-related occupations, whereas the latter is a continuing source of human exposure due to drinking water delivered through Pb-containing pipes or pipes joined with Pb-containing soldering materials (ATSDR, 2015).

While naturally occurring, Pb is not an essential metal and is known to disrupt biological systems by altering molecular interactions, enzyme activities, and cell signaling and function. Acute and chronic exposure to Pb, which is a cumulative toxicant, results in adverse effects in several organ systems (Wani et al., 2015). Young children are particularly vulnerable to the toxic effects of Pb due to negative impacts on the development of the brain and nervous system (Bellinger, 2008a,b). Lead exposure also results in adverse effects in adults, including cardiovascular (Lustberg and Silbergeld, 2002) and kidney (Lanphear et al., 2018; Weaver et al., 2005) effects. Pregnant women exposed to Pb have increased risk for miscarriage, stillbirth, premature birth, and low birth weight (Hertz-Picciotto, 2000; Jelliffe-Pawlowski et al., 2006; Vigeh et al., 2011). Experimental and epidemiological studies have shown that gene-environment interactions likely contribute to variation in susceptibility to adverse effects of Pb exposure (Mitra et al., 2017). However, gene-environment interactions are still poorly characterized because of the multiple confounders and exposure misclassification. While it is widely acknowledged that considerable variation in the susceptibility to harmful effects of Pb exposure exist, there has been little progress identifying susceptibility loci (Cuomo et al., 2022).

Because of the known harmful effects of Pb, regulatory agencies in United States and other countries have established Pb exposure limits and banned its use in specific products, such as Pb-containing gasoline and paints. These actions resulted in a steady decline in blood Pb levels (BLL) in the general population (CDC, 2013). However, epidemiological studies provide evidence of continuing adverse health outcomes even under conditions of diminishing BLL (Wani et al., 2015). The US Centers for Disease Control and Prevention (CDC) concluded that there is sufficient evidence for adverse health effects in children and adults at BLL as low as 5 μg/dl (Hauptman et al., 2017). Consequently, lead exposure is considered an ongoing public health issue, especially in low-income communities with outdated infrastructure or lack of housing upgrades to lead-free paints and pipes (Jacobs et al., 2009; Tong et al., 2000), such as the public health emergency in Flint, Michigan (Zahran et al., 2017). The CDC currently recommends that public health actions be initiated when BLL in a child exceeds 3.5 μg/dl (Burns and Gerstenberger, 2014).

The use of BLL as a surrogate for lead assumes that this biomarker accurately reflects exposure. Experimental studies using animal models have shown that substantial variation exists in BLL for equivalently exposed individuals based on genetic background (Whitfield et al., 2007). Many studies have also shown that genetic background can affect adverse outcomes for environmental contaminants (Rusyn et al., 2022), raising concerns that in the absence of characterization of genetic factors and physiological conditions determining BLL, this biomarker of exposure may have limited utility (Barbosa et al., 2005). Still, BLL remains the most accessible and widely used screening and confirmatory diagnostic tool for evaluating recent or ongoing Pb exposure and is used for public health decision making. However, mitigation and prevention strategies focused on reducing exposure need to account for population variability to ensure protection of more susceptible individuals who may have a greater propensity to accumulate lead.

Given the high degree variability in BLL as well as adverse health outcomes in relation with BLL observed in the human population, we hypothesized that some of this variation is due to genetic and/or environmental differences in exposed individuals. To test this, we took advantage of the Collaborative Cross (CC) mouse genetic reference population to evaluate the impact of genetic and dietary factors that may be contributing to inter-individual variability in Pb biokinetics. We examined BLL accumulation in 49 CC inbred strains exposed to Pb through drinking water while on 2 different diets: standard mouse chow and an American diet designed to recapitulate metabolic conditions of human exposure by matching the nutrient sources and proportions in a typical American diet (Barrington et al., 2018). We quantified the sources of variation in BLL that was dependent on genetics and diet, as well as their interaction. Finally, we performed an initial genetic analysis to identify quantitative trait loci (QTL) contributing to inter-individual variation in BLL and susceptibility to Pb.

Materials and methods

 

 

Experimental design

To characterize the influence of diet and genetics on BLL after Pb exposure, we exposed female mice from 49 CC strains to 1000 ppm Pb while consuming either standard mouse chow or American diet (Barrington et al., 2018) (Figure 1). Blood Pb levels were quantified after 4 weeks of exposure.

Figure 1.

Figure 1.

Open in a new tab

Experimental Design to assess strain, diet, and strain-by-diet on BLL. At 10 weeks of age, 3 females from each of 49 CC strains were placed either on standard mouse chow or American diet. After 1-week acclimation to the diets, mice were exposed to 1000 ppm Pb through drinking water for 4 weeks. At the end of the exposure mice were euthanized, and their blood collected for Pb measurements.

Animals, housing, diet, and exposure

The CC lines (Churchill et al., 2004; Threadgill et al., 2002) were obtained from the Systems Genetics Core Facility at the University of North Carolina at Chapel Hill (UNC) and either used directly for experiments after a 1-week facility and diet acclimation at Texas A&M University (TAMU) or bred inhouse at TAMU prior to Pb exposure. Three females on each diet from each of 49 CC strains were used for a total of 288 mice. Females were selected as the data was also designed to inform future studies on the effects of pre- and per-natal Pb exposure on male and female offspring. Animals were housed at 22 ± 2°C under 12 h light/12 h dark cycle (7:00 am–7:00 pm), weaned at 3 weeks of age in both facilities and given free access to food and water until start of the experiment at 10 weeks of age. Drinking water and feed were examined at UNC and TAMU for metal contamination, which was undetectable at both. At this time, mice from each strain were randomly assigned to 1 of the 2 diet groups: standard rodent chow (Teklald Global Diets, No. 2919) or American diet (Barrington et al., 2018). Both diets had complete coverage of macronutrients as well as amino acid content (chow: protein content 19.2%, fat 9%, carbohydrate 44.7%, fiber 2.6%; American diet: protein content 15.8%, fat 16.6%, carbohydrate 53.8%, fiber 2.6%). Minerals (aluminum, antimony, arsenic, barium, cadmium, chromium, copper, manganese, mercury, phosphorus, selenium, sulfur, thallium, and zinc) were within 2-fold difference between the 2 diets except for iron and magnesium (chow: iron content 200 mg/Kg, magnesium 0.4%; American diet: iron content 61 mg/Kg, magnesium 0.07%). All measurements were done at the Michigan State Veterinary Medical Diagnostic Laboratory using inductively coupled plasma/mass spectrometry (ICP-MS). Both diets were supplemented with a vitamin mix to support mouse health. Lead exposure was started after 1-week acclimation to the diets. At 11 weeks of age, all mice were exposed to 0.1% (1000 ppm) Pb acetate (Sigma Aldrich, No. 467863) through drinking water for 4 weeks ad libitum. Pb dose and time of exposure were chosen to capture the variation in BLL in the CC and to account for its median biologic half-life, respectively (Barbosa et al., 2005). Water consumption was measured daily by recording water bottles weights. The concentration of Pb (mean 731.9 ± 51 ppm SD) and other minerals and metals was measured in water samples by the Michigan State Veterinary Medical Diagnostic Laboratory as noted above. Food consumption was measured in metabolic chambers for CC006 (high BLL) and CC019 (low BLL) and no statistical differences were observed among the 2 strains on either diets (mouse chow: CC006 mean 7.8 g ± 2.0 SD, CC019 7.5 g ± 1.1; American diet: CC006 mean 6.4 g ± 3.8 SD, CC019 7.8 g ± 0.5). At the end of the 4-week Pb exposure, mice were euthanized by carbon dioxide asphyxiation, blood was collected for Pb measurement, and tissues were harvested and immediately flash frozen in liquid nitrogen. All experiments were performed in accordance and approval by Texas A&M University Institution of Animal Care and Use Committee (Protocol 0435-2019).

Phenotypes

Whole-blood samples were collected in 0.5 ml EDTA at necropsy by cardiac puncture and submitted to the Michigan State Veterinary Medical Diagnostic Laboratory for Pb quantification. Lead was measured using ICP-MS. All concentrations are reported in microgram/dl. Other phenotypes were also assessed given their potential effect on BLL. Body weights for each mouse from the CC lines were recorded at 2 different time points: on the first and last day of exposure to Pb. Fat and lean mass were analyzed by quantitative magnetic resonance using an EchoMRI-700 (EchoMRI, Texas) at days 1 and 28 of exposure.

Statistical analysis

Statistical analysis was performed using the software package JMP version 16.2.0 and R statistical software version 4.1.3. When comparing groups, 2-tailed paired or unpaired Student’s t tests were conducted, with p < .05 considered significant. All p values were subjected to a Bonferroni correction. Strain, diet, and strain-by-diet on BLL were also evaluated using a linear mixed effects model to quantify their impacts on the observed BLL variability.

QTL analysis

gQTL, an online resource designed specifically to analyze CC QTLs that contains pre-loaded CC genotypes (Konganti et al., 2018), was used to identify putative QTLs. Mean values for various parameters from each strain were uploaded to the website and QTLs were run using 1000 permutations with “automatic” transformation. Automatic picks either log or square root transformations, whichever normalizes the data best. A permutation test was used to determine the statistical significance of LOD scores. A threshold of p < .05 (after adjustments for multiple testing) was used for significant associations.

Results

 

Inter-strain variability in blood Pb accumulation in response to diet

We observed substantial inter-strain variability in BLL 4 weeks after Pb exposure, with the largest levels detected in CC046/Unc and CC006/TauUnc mice fed mouse chow and American diets, respectively (Figure 2). All strains showed higher BLL on the American diet as compared with standard chow; strains CC0038/GeniUnc, CC045/GeniUnc, CC016/GeniUnc, and CC006/TauUnc showed the largest increases. Notably, CC006/TauUnc had a 58.7-fold increase in BLL on American diet relative to that strain maintained on standard chow, and a 2.9-fold increase in BLL relative to BLL of CC081/Unc on American diet, which was the strain with the second highest BLL on American diet. These data suggest that genetic background and diet both significantly influence BLL in the CC mouse population.

Figure 2.

Figure 2.

Open in a new tab

Phenotypic profile of BLL across 49 CC strains fed either standard mouse chow or American diet after 4 weeks of exposure to high-dose Pb. Floating bars represent the absolute minimum and maximum values of BLL, with a vertical line at the mean. CC strains along the y-axis are shown in ascending order of BLL on standard mouse chow. *Strains that have significant higher BLL on American diet compared with chow.

Comparison of BLL in this study with previous studies

Previously published mouse studies approximating the time, duration, and route of exposure to that used in our study were identified (Jamesdaniel et al., 2018; Jin et al., 2011; Liao et al., 2008; McCabe et al., 1999; Mesdaghinia et al., 2010; Modgil et al., 2019; Togao et al., 2020). Almost all studies retrieved were for a single inbred strain or outbred population with the vast majority being on the BALB/c genetic background. The BLL values reported in these studies were summarized as mean±SD (Figure 3A) to compare with the CC data reported here (Figure 3B). The studies analyzed show a clear dose-response, which appears to depend on genetic background. The sole exception is the outbred Swiss albino mice that have higher BLL (Modgil et al., 2019) relative to BALB/c (Mesdaghinia et al., 2010) exposed to same dose of 200 ppm Pb (Figure 3A). Furthermore, the CC mice fed standard rodent chow have similar BLL, albeit broader range, compared with outbred Swiss albino mice at the same dose of 1000 ppm Pb (Figs. 3A and 3B). Conversely, CC lines fed with American diet at the same Pb dose show much higher BLL compared with mouse chow group (Figure 3B), which compares with BALB/c mice exposed to 2048 ppm Pb (McCabe et al., 1999).

Figure 3.

Figure 3.

Open in a new tab

Phenotypic profile of BLL across previous mouse studies and 49 CC strains fed either standard mouse chow or American diet after 4 weeks of exposure to 1000 ppm Pb. A, BLL are reported as mean±SD for each study in ascending order of lead dose along the y-axis. B, Blood Pb levels in 49 CC strains fed either standard diet or American diet are presented as individual strain values. Quartiles and median are represented by black and red dashed lines, respectively. Two-tailed unpaired Student’s t test is used to determine significance. ****p < .0001.

Partitioning sources of BLL variance

To address the effects of diet, water consumption, body weight, body weight change (measured as difference between body weight and body weight on the first day of exposure), and body composition, we assessed their relative contribution to variation in BLL. On a pairwise basis, we observed statistically significant effects on BLL from diet (Figure 4A), water consumption (Figure 4B), and body weight change (Figure 4D) across all strains but not all strains changes in the same direction. No statistically significant diet effects were observed for body weight (Figure 4C) or body composition (Figure 4E).

Figure 4.

Figure 4.

Open in a new tab

Effect of diet on various phenotypic measurements. Two-tailed paired Student’s t test was used to determine significance for each phenotype. Identical strains are connected by dotted lines. A, Blood Pb levels are presented as mean values, **p = .0038. B, Daily water consumption presented as mean values and expressed as ml/day, ***p = .0004. C, Body weights are presented as mean values, p = .0609. D, Body weight change are presented as mean values, **p = .0072. E, Fat masses are presented as mean values and expressed as percentage, p = .1792. F, Results of multiple linear mixed model showing contributions of each factor to variance in BLL (eta-squared statistic).

A multiple linear mixed model was used to estimate the contribution of each variable to the observed variation in BLL. We found (Figure 4F) that strain has the largest effect on BLL (accounting for 37.9% of BLL variation), followed by diet (32%) and strain-by-diet interactions (17.5%). BLL was highly influenced by genetic background and diet independently (p < .0001), as well as by their interaction (p < .0001). The effects of other covariates, including those different between the standard and American diet, were negligible after accounting for strain, diet, and their interaction.

Genetic mapping of loci that may be influencing BLL in response to diet

After removing CC006/TauUnc due it its extraordinarily high BLL, we performed QTL analysis to identify genetic loci contributing to the phenotypic variation in BLL in response to diet. No significant (p < .05 after multiple testing correction) loci were observed in genome-wide scans for either the standard chow or American diet groups, which is likely indicative to a highly polygenic model controlling BLL. Suggestive diet-specific QTLs were detected for BLL on Chromosome (Chr) 2 and 13 in mice fed standard chow with LOD scores of 5.19 and 5.58, respectively (Figure 5A). For mice fed the American diet, suggestive QTLs were associated on Chr 1 and 5 with LOD scores of 5.76 and 6.42, respectively (Figure 5B).

Figure 5.

Figure 5.

Open in a new tab

Genetic mapping of loci controlling BLL in response to diet. A, QTL scan for BLL on standard chow. B, QTL scan for BLL on American diet. The x-axis is the genome location; y-axis is the logP of association between locus and BLL. C, Founder contributions to standard chow diet BLL and haplotype analysis on Chr 2. D, Founder contributions to standard chow diet BLL and haplotype analysis on Chr 13. E, Founder contributions to American diet BLL and haplotype analysis on Chr 1. F, Founder contributions to American diet BLL and haplotype analysis on Chr 5. Each of the 8 founders is represented in a different color. The mouse genome location is on the x-axis and y-axis shows the founder estimated effect on BLL after Pb and diet exposure.

Because the genome of each CC strain is a distinct mosaic of the haplotypes from the original 8 founder strains, the impact of any given QTL in the locus can be described in terms of strain effects, that is, the estimated substitution effects of the 8 founder haplotypes. The strain effect at suggestive QTL peaks on Chr 2 and 13 (Figs. 5C and 5D) include a strong positive effect on BLL by the PWK/PhJ strain haplotype and a negative effect by strains CAST/EiJ and NZO/HILtJ, respectively. Similarly, the PWK/PhJ strain haplotype drives the high BLL in mice fed American diet for both suggestive QTL peaks on Chr 1 and 5, whereas the CAST/EiJ and NZO/HILtJ strain haplotypes are associated with lower BLL in the same genomic regions (Figs. 5E and 5F).

Discussion

Exposure assessment through BLL is currently used to quantify risk to human health and to assess the effectiveness of current strategies for decreasing Pb environmental levels (Nag and Cummins, 2022). However, variation in BLL reported in humans suggests a complex interplay between risk factors such as genetics, age, and socio-economic status (Marshall et al., 2020; McFarland et al., 2022; Whitfield et al., 2007). We show the complexity of the genetic architecture of BLL accumulation in response to Pb exposure through drinking water and the impact of diet using the human-relevant high-fat/high-carbohydrate American diet using the CC population-based mouse model.

The current study demonstrated that CC mice can be used to identify factors contributing to inter-individual variation in BLL accumulation. The 49 CC strains tested exhibited a wide range of BLL after 4 weeks of exposure reflecting their genetic diversity. Variation in BLL increased, sometimes substantially, when mice were fed the American diet, suggesting that diet also influences BLL. Inadequate nutrition such as iron and calcium deficiencies have been reported to influence Pb absorption (Cuomo et al., 2022), with diets lacking certain nutrients associated with increased Pb absorption. Our American diet was designed to recapitulate exposure in human populations known to be deficient in some minerals. Nonetheless, we observed a high degree of variation in BLL of mice fed American diet, suggesting that genetic background plays a crucial role in blood Pb accumulation that was independent of diet since not all strains had a similar diet effect. The high variability in BLL explained by the genetic background is likely due to genetic polymorphisms that modulate Pb ADME, as well as epigenetic differences across strains, that are responsive to environmental factors such as diet (Cuomo et al., 2022). We identified CC006/TauUnc as an extreme, diet-dependent strain in terms of BLL levels. Our results clearly demonstrated that genetic background, diet, and their interaction explain a large fraction of the variation in BLL, independent of other factors such as water consumption, body weight, body weight change, or body composition. It is not clear why across the CC population mice drink less water on the American diet than when fed the standard diet. However, this did not contribute to variation in BLL. The large effect of diet suggests that many mouse-based toxicological studies that do not account for metabolic status, such as on American diet like most exposed humans, likely under-estimate true exposure effects.

While we observed clear strain-specific effects and a large degree of inter-strain variability, QTL mapping revealed only suggestive diet-specific QTLs for BLL on Chr 1 and 13 when on standard mouse chow and Chr 1 and 5 when on American diet. The low number of the strains used in this study, small effect sizes of the QTL, and a large number of QTL impacting BLL may have limited the statistical power of this analysis. Nonetheless, it is notable that the suggestive QTLs that were detected all involved alleles contributed by the wild derived PWK/PhJ strain, highlighting the importance of wild-derived founder contribution in the CC. By exploring broader genetic variation, the CC population has suggested novel loci for differential blood Pb accumulation and shows that diet contributes to genetic-related variation in BLL.

Understanding variation in BLL is especially important because regulatory standards are set on environmental or exposure levels modeled using estimates of BLL. For example, EPA’s exposure-dose modeling approach is used to set drinking water concentrations to ensure that the 97.5th percentile of BLL is below a reference level (Zartarian et al., 2017), now set at 3.5 µg/dl by CDC in United States. This model-based approach incorporates an assumption used in the Integrated Exposure Uptake and Biokinetic model (White et al., 1998) that for fixed drinking water exposure concentration, BLL varies with a geometric standard deviation (GSD)=1.6. The value of 1.6 is based on analyses from the late 1990s using epidemiologic data on children’s BLL (Hogan et al., 1998). Implementing this approach under the “default” assumption of GSD = 1.6 results in an exposure limit that is set so that only 2.5% of the population would fall above the action level (Figure 6A). If the actual GSD was 1.37, based on the CC mice fed the standard diet, then as few as 0.2% of the population would actually be above the action level (Figure 6B). Thus, under this scenario, the exposure limit based on the current default would be over-protective by 1.4-fold. By contrast, if the actual GSD was 2.3, based on the CC mice fed the American diet that is likely more relevant to the U.S. population, then as much as 13.4% of the population would actually be above the action level and the default exposure limit would be under-protective by 2-fold (Figure 6C). Thus, the degree of inter-individual variation influenced by diet is highly impactful to the setting of regulatory or risk management interventions to reduce lead exposure.

Figure 6.

Figure 6.

Open in a new tab

Illustration of the impact of default assumptions using population variation in BLL. A, Exposure limit under the constraint that no more than 2.5% of the population can exceed the CDC Action Level of 3.5 µg/dl, using the U.S. EPA default assumption of GSD = 1.6 for population variation. B, Distribution of BLL if the population variation was equal to that modeled by the CC mice fed a standard diet (GSD = 1.37). C, Distribution of BLL if the population variation was equal to that modeled by the CC mice fed an American diet (GSD = 2.3).

There are several important limitations to the present study that need to be acknowledged, particularly in the study design. This study was not designed to capture sex-specific outcomes as we only utilized female mice for the study. In addition, the mouse population examined here is the equivalent to an adult population, whereas the Pb default GSD assumption was derived from data in children. Further studies should consider testing both sexes and different windows of exposure to further refine these estimates. Another key area that needs further exploration is the variation of BLL due to a wider range of diets, as our study tested just 2 diets. Thus, the impact of diet variation in humans is likely to be much greater than the results here. Nonetheless, this experimental study takes an important step towards quantifying the importance and impact of genetics and diet on Pb risk assessment strategies that can be used in refining the evaluation of Pb action levels in humans.

Conclusions

Human BLL biomonitoring provides an empirical measure of Pb exposure reflecting the body burden, but risk mitigation requires relating BLL back to exposure. In the current study, we found that for Pb exposure delivered through drinking water, BLL is highly variable, and strongly influenced by both genetics and diet in an adult mouse genetic reference population. Importantly, we found that in mice fed the equivalent of the American diet, the degree of variation in BLL exceeded the assumption used in most risk assessments supporting regulatory and cleanup actions to reduce Pb exposure. These results suggest that risk assessment and/or Pb action levels that are based on current default assumptions for the variation in BLL biokinetics may fail to adequately protect more sensitive individuals with a greater propensity to accumulate Pb. This work highlights the importance of implementing Pb risk assessment strategies and action levels that account for the variation in BLL as a result of genetic and diet interactions. Future studies should focus on identifying the polymorphic genes and mechanisms underlying diet-dependent variation in BLL and identification of biomarkers that better reflect actual exposure to better understand the relationship between BLL and adverse health outcomes from Pb exposure to protect sensitive populations.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgments

We thank Dr Rachel Lynch and the rodent preclinical phenotyping core (RPPC) of the Texas A&M Institute for Genome Sciences and Society (TIGSS) for technical support in conduct of the in-life portion of the study.

Contributor Information

Danila Cuomo, Department of Cell Biology and Genetics, Texas A&M University, College Station, Texas, USA.

Megan Nitcher, Department of Cell Biology and Genetics, Texas A&M University, College Station, Texas, USA.

Estefania Barba, Department of Cell Biology and Genetics, Texas A&M University, College Station, Texas, USA.

Andrew P Feinberg, Center for Epigenetics, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Center for Epigenetics, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Center for Epigenetics, Department of Mental Health, Johns Hopkins University, Baltimore, Maryland, USA.

Ivan Rusyn, Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.

Weihsueh A Chiu, Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.

David W Threadgill, Department of Cell Biology and Genetics, Texas A&M University, College Station, Texas, USA; Department of Nutrition, Texas A&M University, College Station, Texas, USA.

Funding

This study was funded, in part, by grants from the National Institutes of Health (RM1 HG008529, P30 ES029067, R01 ES029911, and P42 ES027704).

References

  1. ATSDR. 2015. Taking an Exposure History. Agency for Toxic Substances and Disease Registry, Department of Health and Human Services, Atlanta, GA. [Google Scholar]
  2. Barbosa F. Jr., Tanus-Santos J. E., Gerlach R. F., Parsons P. J. (2005). A critical review of biomarkers used for monitoring human exposure to lead: Advantages, limitations, and future needs. Environ. Health Perspect. 113, 1669–1674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barrington W. T., Wulfridge P., Wells A. E., Rojas C. M., Howe S. Y. F., Perry A., Hua K., Pellizzon M. A., Hansen K. D., Voy B. H., et al. (2018). Improving metabolic health through precision dietetics in mice. Genetics 208, 399–417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bellinger D. C. (2008a). Neurological and behavioral consequences of childhood lead exposure. PLoS Med. 5, e115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bellinger D. C. (2008b). Very low lead exposures and children’s neurodevelopment. Curr. Opin. Pediatr. 20, 172–177. [DOI] [PubMed] [Google Scholar]
  6. Burns M. S., Gerstenberger S. L. (2014). Implications of the new centers for disease control and prevention blood lead reference value. Am. J. Public Health 104, e27-33–e33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. CDC. (2013). Very high blood lead levels among adults – United States, 2002-2011. MMWR Morb. Mortal Wkly. Rep. 62, 967–971. [PMC free article] [PubMed] [Google Scholar]
  8. Churchill G. A., Airey D. C., Allayee H., Angel J. M., Attie A. D., Beatty J., Beavis W. D., Belknap J. K., Bennett B., Berrettini W., et al. (2004). The collaborative cross, a community resource for the genetic analysis of complex traits. Nat. Genet. 36, 1133–1137. [DOI] [PubMed] [Google Scholar]
  9. Cuomo D., Foster M. J., Threadgill D. (2022). Systemic review of genetic and epigenetic factors underlying differential toxicity to environmental lead (Pb) exposure. Environ. Sci. Pollut. Res. Int. 29, 35583–35598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Hauptman M., Bruccoleri R., Woolf A. D. (2017). An update on childhood lead poisoning. Clin. Pediatr. Emerg. Med. 18, 181–192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hertz-Picciotto I. (2000). The evidence that lead increases the risk for spontaneous abortion. Am. J. Ind. Med. 38, 300–309. [DOI] [PubMed] [Google Scholar]
  12. Hogan K., Marcus A., Smith R., White P. (1998). Integrated exposure uptake biokinetic model for lead in children: Empirical comparisons with epidemiologic data. Environ. Health Perspect. 106(Suppl 6), 1557–1567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Jacobs D. E., Wilson J., Dixon S. L., Smith J., Evens A. (2009). The relationship of housing and population health: A 30-year retrospective analysis. Environ. Health Perspect. 117, 597–604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Jamesdaniel S., Rosati R., Westrick J., Ruden D. M. (2018). Chronic lead exposure induces cochlear oxidative stress and potentiates noise-induced hearing loss. Toxicol. Lett. 292, 175–180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jarup L. (2003). Hazards of heavy metal contamination. Br. Med. Bull. 68, 167–182. [DOI] [PubMed] [Google Scholar]
  16. Jelliffe-Pawlowski L. L., Miles S. Q., Courtney J. G., Materna B., Charlton V. (2006). Effect of magnitude and timing of maternal pregnancy blood lead (Pb) levels on birth outcomes. J. Perinatol. 26, 154–162. [DOI] [PubMed] [Google Scholar]
  17. Jin Y., Yu F., Liao Y., Liu S., Liu M., Xu J., Yang J. (2011). Therapeutic efficiency of succimer used with calcium and ascorbic acid in the treatment of mild lead-poisoning. Environ. Toxicol. Pharmacol. 31, 137–142. [DOI] [PubMed] [Google Scholar]
  18. Konganti K., Ehrlich A., Rusyn I., Threadgill D. W. (2018). gQTL: A web application for QTL analysis using the collaborative cross mouse genetic reference population. G3 (Bethesda) 8, 2559–2562. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lanphear B. P., Rauch S., Auinger P., Allen R. W., Hornung R. W. (2018). Low-level lead exposure and mortality in US adults: A population-based cohort study. Lancet. Public Health 3, e177–e184. [DOI] [PubMed] [Google Scholar]
  20. Liao Y., Zhang J., Jin Y., Lu C., Li G., Yu F., Zhi X., An L., Yang J. (2008). Therapeutic potentials of combined use of DMSA with calcium and ascorbic acid in the treatment of mild to moderately lead intoxicated mice. Biometals 21, 1–8. [DOI] [PubMed] [Google Scholar]
  21. Lustberg M., Silbergeld E. (2002). Blood lead levels and mortality. Arch. Intern. Med. 162, 2443–2449. [DOI] [PubMed] [Google Scholar]
  22. Marshall A. T., Betts S., Kan E. C., McConnell R., Lanphear B. P., Sowell E. R. (2020). Association of lead-exposure risk and family income with childhood brain outcomes. Nat. Med. 26, 91–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. McCabe M. J. Jr., Singh K. P., Reiners J. J. Jr. (1999). Lead intoxication impairs the generation of a delayed type hypersensitivity response. Toxicology 139, 255–264. [DOI] [PubMed] [Google Scholar]
  24. McFarland M. J., Hauer M. E., Reuben A. (2022). Half of US population exposed to adverse lead levels in early childhood. Proc. Natl. Acad. Sci. U.S.A. 119, e2118631119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mesdaghinia A., Yazdanpanah H., Seddighi M., Banafshe H. R., Heydari A. (2010). Effect of short-term lead exposure on PTZ-induced seizure threshold in mice. Toxicol. Lett. 199, 6–9. [DOI] [PubMed] [Google Scholar]
  26. Mitra P., Sharma S., Purohit P., Sharma P. (2017). Clinical and molecular aspects of lead toxicity: An update. Crit. Rev. Clin. Lab. Sci. 54, 506–528. [DOI] [PubMed] [Google Scholar]
  27. Modgil S., Bammidi S., Banik A., Sharma V. L., Anand A. (2019). Alpha lipoic acid attenuates the long-term effects of lead exposure in retinal ischemic injury mouse model. Environ. Sci. Pollut. Res. Int. 26, 15548–15558. [DOI] [PubMed] [Google Scholar]
  28. Nag R., Cummins E. (2022). Human health risk assessment of lead (Pb) through the environmental-food pathway. Sci. Total Environ. 810, 151168. [DOI] [PubMed] [Google Scholar]
  29. Rusyn I., Chiu W. A., Wright F. A. (2022). Model systems and organisms for addressing inter- and intra-species variability in risk assessment. Regul. Toxicol. Pharmacol. 132, 105197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Threadgill D. W., Hunter K. W., Williams R. W. (2002). Genetic dissection of complex and quantitative traits: From fantasy to reality via a community effort. Mamm. Genome 13, 175–178. [DOI] [PubMed] [Google Scholar]
  31. Togao M., Nakayama S. M. M., Ikenaka Y., Mizukawa H., Makino Y., Kubota A., Matsukawa T., Yokoyama K., Hirata T., Ishizuka M. (2020). Bioimaging of Pb and STIM1 in mice liver, kidney and brain using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and immunohistochemistry. Chemosphere 238, 124581. [DOI] [PubMed] [Google Scholar]
  32. Tong S., McMichael A. J., Baghurst P. A. (2000). Interactions between environmental lead exposure and sociodemographic factors on cognitive development. Arch. Environ. Health 55, 330–335. [DOI] [PubMed] [Google Scholar]
  33. Vigeh M., Yokoyama K., Seyedaghamiri Z., Shinohara A., Matsukawa T., Chiba M., Yunesian M. (2011). Blood lead at currently acceptable levels may cause preterm labour. Occup. Environ. Med. 68, 231–234. [DOI] [PubMed] [Google Scholar]
  34. Wani A. L., Ara A., Usmani J. A. (2015). Lead toxicity: A review. Interdiscip. Toxicol. 8, 55–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Weaver V. M., Schwartz B. S., Jaar B. G., Ahn K. D., Todd A. C., Lee S. S., Kelsey K. T., Silbergeld E. K., Lustberg M. E., Parsons P. J., et al. (2005). Associations of uric acid with polymorphisms in the delta-aminolevulinic acid dehydratase, vitamin D receptor, and nitric oxide synthase genes in Korean lead workers. Environ. Health Perspect. 113, 1509–1515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. White P. D., Van Leeuwen P., Davis B. D., Maddaloni M., Hogan K. A., Marcus A. H., Elias R. W. (1998). The conceptual structure of the integrated exposure uptake biokinetic model for lead in children. Environ. Health Perspect. 106(Suppl 6), 1513–1530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Whitfield J. B., Dy V., McQuilty R., Zhu G., Montgomery G. W., Ferreira M. A., Duffy D. L., Neale M. C., Heijmans B. T., Heath A. C., et al. (2007). Evidence of genetic effects on blood lead concentration. Environ. Health Perspect. 115, 1224–1230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zahran S., McElmurry S. P., Sadler R. C. (2017). Four phases of the flint water crisis: Evidence from blood lead levels in children. Environ. Res. 157, 160–172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Zartarian V., Xue J., Tornero-Velez R., Brown J. (2017). Children’s lead exposure: A multimedia modeling analysis to guide public health decision-making. Environ. Health Perspect. 125, 097009. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Toxicological Sciences are provided here courtesy of Oxford University Press

RESOURCES