Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population
© Ellis et al; licensee BioMed Central Ltd. 2012
Received: 22 November 2011
Accepted: 19 June 2012
Published: 19 June 2012
The 'exposome' represents the accumulation of all environmental exposures across a lifetime. Top-down strategies are required to assess something this comprehensive, and could transform our understanding of how environmental factors affect human health. Metabolic profiling (metabonomics/metabolomics) defines an individual's metabolic phenotype, which is influenced by genotype, diet, lifestyle, health and xenobiotic exposure, and could also reveal intermediate biomarkers for disease risk that reflect adaptive response to exposure. We investigated changes in metabolism in volunteers living near a point source of environmental pollution: a closed zinc smelter with associated elevated levels of environmental cadmium.
High-resolution 1H NMR spectroscopy (metabonomics) was used to acquire urinary metabolic profiles from 178 human volunteers. The spectral data were subjected to multivariate and univariate analysis to identify metabolites that were correlated with lifestyle or biological factors. Urinary levels of 8-oxo-deoxyguanosine were also measured, using mass spectrometry, as a marker of systemic oxidative stress.
Six urinary metabolites, either associated with mitochondrial metabolism (citrate, 3-hydroxyisovalerate, 4-deoxy-erythronic acid) or one-carbon metabolism (dimethylglycine, creatinine, creatine), were associated with cadmium exposure. In particular, citrate levels retained a significant correlation to urinary cadmium and smoking status after controlling for age and sex. Oxidative stress (as determined by urinary 8-oxo-deoxyguanosine levels) was elevated in individuals with high cadmium exposure, supporting the hypothesis that heavy metal accumulation was causing mitochondrial dysfunction.
This study shows evidence that an NMR-based metabolic profiling study in an uncontrolled human population is capable of identifying intermediate biomarkers of response to toxicants at true environmental concentrations, paving the way for exposome research.
Keywordsmetabonomics cadmium environmental health exposome metabolomics molecular epidemiology
From the point of conception and throughout life, humans experience a broad range of physical, chemical and biological exposures. The health effects of such exposures will depend not only on dose but also on their interaction with each other and with the characteristics of the individual, such as age, sex and genotype. Hence, it is a persistent and significant challenge to understand how specific environmental factors produce effects on human health. Biomarkers already play an important role in characterizing both dose and effect; however, their full potential remains to be explored. Molecular profiling technologies ('-omics') have been suggested to be an important route to the discovery of novel biomarkers to improve exposure assessment in man . Such techniques can also report on the biological consequences of exposure by identifying intermediate biomarkers that both correlate with exposure and predict health endpoints, termed a 'meet-in-the-middle' approach [2, 3]. Such markers would help to define the mechanism of toxicity and the etiology of disease in the human population and ultimately could inform follow-up surveillance for communities impacted by environmental pollution. It has been proposed that by combining a wide range of molecular profile data, including parallel assays of molecular adducts, metals and other exogenous species, we have the potential to define the molecular imprint of the totality of environmental exposures in an individual's lifetime, the 'exposome'[4, 5].
Metabolic profiling allows the study of an individual's metabolic phenotype [6, 7] and represents a systematic and efficient route to intermediate biomarkers. It can detect and classify the consequences of toxicant exposure in vivo [8, 9] and the approach readily translates to molecular epidemiology . Here we use an NMR-based metabolic profiling approach to investigate changes in human systemic metabolism from a sample of the population living near a point source of environmental pollution. The site (Avonmouth, UK) was home to one of the world's largest smelters and large amounts of cadmium (Cd) and other potentially toxic elements were released into the local environment until its closure in early 2003 resulting in the presence of elevated concentrations of metals in air, soil and house dust in the area .
Cd is a toxic heavy metal of occupational and environmental concern due to its widespread contamination of sites worldwide [12, 13] and long biological half-life (10 to 30 years) . Cd exposure is associated with a host of adverse effects , including osteoporosis, pulmonary dysfunction, hypertension and nephropathy. The kidney accumulates Cd and is the critical organ, particularly at environmental levels of exposure, with tubular proteinuria being an early effect of Cd exposure . This proteinuria is usually detected as an increased excretion of low-molecular weight urinary proteins, such as N-acetyl-β-D-glucosaminidase (NAG). There is some evidence to suggest that, following high and/or prolonged exposure, these pre-clinical changes may progress to renal impairment with a decreased glomerular filtration rate [17–19], and eventually to renal failure .
A further concern is cancer risk; Cd is a multi-site carcinogen in both rodents  and humans  and as such has been classified as a human carcinogen by the International Agency for Research on Cancer  and the National Toxicology Program . The mechanism of Cd carcinogenesis is thought to be multi-factorial but is as yet poorly understood. Cd has been shown to possess estrogenic activity [25–27], inhibit mismatch repair , alter DNA methylation [29, 30] and increase reactive oxygen species (ROS) production .
The diet is the major source of Cd exposure in the general non-smoking population with around 5% to 10%  absorption from the gastrointestinal tract. However, among smokers tobacco is also a significant source of exposure owing to the high rate of absorption from the lungs (10 to 50%) . The low excretion rate of Cd and the resulting long biological half life means that Cd body burden increases slowly with age and appears to accumulate at a higher rate in women compared to men. Urinary-Cd (U-Cd) concentration is mainly influenced by the total body burden and is, hence, a marker for long term exposure, but can also increase as kidney damage occurs .
Sample collection and epidemiology data
One hundred and eighty adult participants were recruited to the current study as described in Thomas et al. (2009) , and 178 samples in total were analyzed by 1H NMR spectroscopy. Briefly, each individual was asked to provide one spot morning urine sample; each sample was frozen on the day of collection. Each volunteer completed a lifestyle questionnaire that provided information on a number of parameters including smoking status (current, past, never), age and sex. Data were anonymized for the present study. U-Cd and U-NAG were previously determined  and were both log-normally distributed. Ethical approval for the study was granted by Bristol (UK) South and Central Ethics Committee and all participants provided their written informed consent before data collection.
NMR data acquisition
Samples were thawed on ice and prepared in a single batch. For each sample, an aliquot (0.4 mL) was mixed with buffer (0.2 mL, 0.2 M sodium phosphate, pH 7.4) containing 2H2O (20% v/v) and sodium 3-trimethylsilyl-1-[2, 2, 3, 3,-2H4] propionate (TSP, 0.3 mM) and centrifuged (16,000 g, ten minutes) to remove suspended particulate matter.
High-resolution 1H NMR spectroscopy experiments were conducted using a Bruker AVANCE 600 spectrometer (Bruker Biospin, Rheinstetten, Germany) fitted with a flow-injection mode probehead (5 mm FI TXB 1H-13C/15N-2H Z-GRD H8432/K0201 Z8432/0201, Bruker) at a field strength of 14.1 T (600.13 MHz 1H frequency). The probe temperature was set to 300 K. Samples were delivered to the probehead in 96-well plates using an automated flow injection system (Bruker). Following introduction to the probe, samples were left to equilibrate (three minutes) prior to gradient shimming using the 1H channel to ensure good magnetic field homogeneity. All liquid sample handling, automation and acquisition were controlled using XWIN-NMR 3.1 software (Bruker) running on a UNIX workstation (Silicon Graphics, USA).
One-dimensional NMR spectra were acquired using a standard pulse sequence using excitation sculpting with gradients to suppress the water resonance . Following eight dummy scans, each spectrum was acquired into 64 k datapoints over a spectral width of 20 ppm as the sum of 64 transients. The acquisition time was 2.73 seconds, giving a native free induction decay (FID) resolution of 0.183 Hz. The relaxation delay was set at two seconds. The total acquisition time was approximately six minutes per sample. An apodization function equivalent to a line-broadening of 0.3 Hz was applied to each FID prior to Fourier transformation. Assignment of peaks to specific metabolites was based on the addition of known standards to the biological samples, together with published literature , on-line metabolomics databases and statistical total correlation spectroscopy (STOCSY) . Additional NMR spectroscopy experiments were conducted to assign a doublet at a chemical shift of δ 1.11. A one-dimensional 1H selective TOCSY experiment (See additional file 1, Figure S1) observed three related 1H resonances in the same molecule. Further two-dimensional experiments [See additional file 1, Figure S2] allowed the full 1H and 13C characterization [See additional file 1, Table S1] of the two diastereoisomers of 2,3-dihydroxybutanoic acid [See additional file 1, Figure S3]: 4-deoxy-erythronic acid and 4-deoxy-threonic acid, both of which have previously been identified in human urine .
NMR data processing and statistical analysis
Data were imported and manipulated in Matlab (Mathworks, Natick, MA, USA) using in-house code for automatic phasing, baseline correction, and referencing chemical shifts to the TSP resonance at δ 0. To account for variance in dilution of the urine samples each spectrum was subsequently normalized by the median-fold change to a reference spectrum generated by calculation of the median of all spectra at each spectral point . The spectra were analyzed at two resolutions: (1) reduced resolution of 0.01 ppm width (1,127 data points), for partial least squares (PLS) regression analysis; and (2) high resolution (32,697 data points), for covariance/correlation analysis.
For multivariate analysis the reduced resolution data were exported to SIMCA-P+ (Umetrics) with the TSP (δ < 0.15) and residual water (δ 4.675 to 4.895) resonances removed. PLS regression analysis was applied to these data to model optimally the association between the metabolic profile and the lifestyle and biological factors (U-Cd, age, sex, U-NAG and smoking status) and to identify individual metabolites significantly contributing to any associations observed. Permutation analysis was carried out for each PLS model to test for validity using 1,000 permutations per test [See additional file 1, Figure S4].
Additionally, we used a variable-wise covariance and correlation based analysis of the high resolution data to assist in the identification of individual metabolites associated with lifestyle/biological factors. The results were visualized by color-scale projection onto the plots to indicate the correlation of metabolites with the individual factors, with red indicating a high correlation and dark blue no correlation. The direction and magnitude of the signals represent the co-variation of the metabolites with the specific lifestyle factor.
We measured the area under selected spectral peaks for six specific metabolites (3-hydroxyisovalerate (3-HV), dimethylglycine (DMG), citrate, 4-deoxy-erythronic acid (4-DEA), creatinine and creatine) identified from the above analyses. These data were then analyzed by multiple linear regression (MLR) using SPSS (v19, IBM, Armonk, NY, USA) controlling for major confounding factors (age, sex and smoking status). We developed three separate MLR models for citrate. Model 1 excluded individuals who did not answer the question regarding smoking status (n = 3); model 2 additionally excluded all individuals classed as current smokers (n = 32); and model 3 additionally excluded current (n = 32) and past smokers (n = 35). Additionally, we used non-parametric tests (Kruskal-Wallis/Mann-Whitney or Spearman's ρ) to test for a significant (P < 0.05) difference or trend, respectively, in the concentration of citrate between the three classes of smoker (current-, past-, and never smoked). A normal distribution of values in each group in the dataset was observed (P < 0.05, Shapiro-Wilk test).
Quantitation of the urinary biomarker 8-oxo-deoxyguanosine (8-oxodG)
Urinary-8-oxodG (U-8-oxodG) was quantified in samples from the upper and lower 15th percentile according to U-Cd concentration, as described previously . The U-8-oxodG concentrations were normalized to the creatinine concentration (taken from ) and a non-parametric Mann-Whitney test used to test for a significant (P < 0.05) difference between the groups. A non-normal distribution of values in the upper 15th percentile group was observed (P < 0.05, Shapiro-Wilk test).
Effects of Cd exposure on endogenous metabolism
Statistics of multivariate analysis models demonstrating an association between 1H NMR spectroscopic data and several biological and lifestyle factors.
Number of LVs
(current smokers excluded)
(past and current smokers excluded)
Since smoking is a major source of cadmium exposure we excluded smokers from PLS modelling to test if the link between metabolite profiles and U-Cd was independent of smoking status. PLS models with significant prediction of U-Cd could be generated either with current smokers excluded (Q2 = 0.330, P < 0.001, Table 1) or with both current smokers and past smokers excluded (Q2 = 0.142, P < 0.001, Table 1), suggesting that exposure to Cd from environmental and dietary sources alone may be sufficient to produce detectable metabolic effects.
Association between other factors and endogenous metabolism
It has already been shown in this population that U-Cd is significantly associated with age, sex, U-NAG and smoking status . In order to obtain a better understanding of possible causal links and the specificity of the responses to Cd we conducted further pattern recognition analyses to attempt to derive models for these potentially confounding factors. We could predict a statistically significant degree of variation by PLS regression in each of: age (Q2 = 0.224, P < 0.001), smoking status (Q2 = 0.185, P < 0.01) and U-NAG levels (Q2 = 0.162, P < 0.001) from the urinary NMR data (Table 1). Inspection of regression weights for each of these models indicated that several metabolites correlated with U-Cd levels (that is, 3-HV, DMG, 4-DEA and creatinine) were also associated with these other physiological and lifestyle factors. Further specific metabolite correlates were defined, including cotinine to smoking status and creatine to sex (Figures 2B-2E).
Regression analysis of individual urinary metabolite levels
Correlation coefficients (standardized beta values) derived from multiple linear regression of key metabolite to lifestyle factors.
Correlation to Y variable
1.276 to 1.269
2.936 to 2.920
2.515 to 2.590
2.515 to 2.590
2.515 to 2.590
4.073 to 4.040
3.940 to 3.930
1.118 to 1.108
Association of heavy metal exposure and oxidative stress
Alterations in citrate levels could imply changes in the consumption of citrate via the Krebs cycle in the mitochondrion. A perturbation to oxidative metabolism would be expected to lead to a change in ROS production and accompanying oxidative stress. Cd has been shown to increase ROS production in vivo [31, 44] and indirectly damages DNA in vitro via oxidation by hydrogen peroxide production . We attempted to define the association of heavy metal exposure (using U-Cd as an indicator) and the potential for oxidative DNA damage by measuring U-8-oxodG, which is widely used as a biomarker of oxidative stress [46, 47]. We measured U-8-oxodG in a subset of 40 individuals (the upper and lower 15% of volunteers according to U-Cd concentration (Figure 3B)). The mean U-Cd concentration in the lower group was 0.115 ± 0.006 (nmol/mmol creatinine ± standard error of the mean (SEM)) and 0.766 ± 0.052 in the higher group. There was a significant (P < 0.05, Mann-Whitney test) increase in U-8-oxodG concentration (median ± SD) in the individuals in the upper 15th percentile group (1.77 ± 0.82 pmol/mmol creatinine), compared to those in the lower group (1.39 ± 0.45 pmol/mmol creatinine). This suggests a positive association between U-8-oxodG and U-Cd, which could be representative of mitochondrial dysfunction and increased ROS production, consistent with our hypothesis.
Mechanistic evidence of chemical toxicity is vital to allow accurate risk assessment; in this context, multiple intermediate biomarkers may serve as combinatorial signatures of early molecular events of toxicity. Metabolic phenotypes can be used as an information-rich endpoint in molecular epidemiology and can define the interactions of lifestyle, environment and genes that determine diseases . We have shown here that metabolic profiling can be successfully applied to a human toxicological/molecular epidemiological study, specifically in a population exposed to Cd pollution derived through environmental exposure, to define associations between the metabolome, lifestyle and exposure data, and other molecular biomarkers of tissue damage.
U-Cd concentration in the majority of individuals living at the Avonmouth site was within typical estimated levels of a normal population (0.1 to 0.6 nmol/mmol creatinine ), and only 25% of individuals had U-Cd levels above 0.5 nmol/mmol creatinine  which is the lower limit of potential risk of renal damage suggested by the Scientific Committee on Toxicity, Ecotoxicity and the Environment  (CSTEE). The joint FAO/WHO expert committee on food additives (JECFA) concluded that an excess prevalence of tubular renal dysfunction would not be expected below a U-Cd level of 2.5 nmol/mmol creatinine , considerably higher than the range of U-Cd concentrations at which we observed metabolic changes. An NMR-based metabolic profiling approach could complement exposure assessment as it details actual biochemical disturbance and could aid the determination of the mechanism of action of a chemical toxicant and the etiology of associated disease.
We identified a previously unreported association of urinary citrate to both Cd exposure and smoking. Surprisingly, citrate was positively correlated with U-Cd and negatively with smoking status, despite smoking being an important source of cadmium exposure. The increase in urinary citrate levels associated with U-Cd may result from Cd-induced tubule toxicity that affects citrate handling in the kidney , although we did not observe a direct association between citrate levels and tubular dysfunction (as represented by U-NAG levels). Smoking may reduce urinary citrate levels due to reduced capacity for oxidative metabolism in mitochondria. Chronic smoking is associated with a decrease of complex IV and III activity in the lymphocyte mitochondrial electron transfer chain, which returns to normal after cessation of smoking [51, 52]. There was also a positive association between U-Cd and U-8-oxodG, which is indicative of increased systemic oxidative stress and may also result from mitochondrial dysfunction. Past smokers also had intermediate urinary citrate levels relative to current smokers and those who had never smoked. This observation suggests that it may be possible to monitor biological effects of exposure in individuals who are no longer exposed to the causative agent, whether they be irreversible or slow to return to a 'normal' metabolic phenotype.
Using a non-targeted approach 3-HV was found to be negatively correlated to U-Cd, age and sex, and DMG negatively correlated to U-Cd and sex. However, after allowing for confounding factors it was not conclusively shown that these two metabolites were associated with U-Cd, possibly due to the limitation of sample numbers. Despite this, correspondence between our observations and other studies of the biochemical effects of Cd suggest that these metabolic changes may still be related to the underlying mechanisms of Cd toxicity. Cd inhibits oxidoreductases involved in vitamin D metabolism  and 3-HV is a product of the L-leucine mitochondrial catabolic pathway which also involves this class of enzymes. One hypothesis is that reduction in urinary 3-HV could be occurring in parallel to direct mitochondrial damage [31, 54]. Another of the six metabolites identified as correlated to U-Cd, 4-DEA, is also involved in amino acid catabolism: it is a breakdown product of threonine. Threonine deaminase converts threonine to alpha-ketobutyrate, a precursor of isoleucine, and this is further modified by reductases to 4-deoxyerythronic acid and its diastereoisomer 4-deoxythreonic acid  (also observed in our study but not correlated to U-Cd).
Our study had several limitations: clearly our observations need to be replicated in other larger cohorts and we did not have available repeated or alternative biological specimens from the study participants. Metabolite measurements in blood plasma for instance would provide clarification as to whether the observed associations were systemic in origin or likely to reflect mainly renal effects. A detailed analysis of the contribution of diet and activity to specific metabolite variation was also not possible. These issues are currently being examined in follow-up studies (for example, http://www.envirogenomarkers.net). In particular it will be important to evaluate the additional prognostic value of metabolomic measurements compared to established clinical pathology data and exposure assessment if the metabolites observed here associated with Cd burden are to be considered as valuable biomarkers in the future.
Using an NMR-based approach, we have demonstrated the capacity, in principle, of metabolic profiling to characterize the metabolic consequences of exposure to environmental toxicants, such as Cd and tobacco smoke, in a human population. We conclude that metabolic profiling has the potential to identify novel biomarkers and molecular signatures of the effects of exposure to many environmental toxicants, and thus improve risk assessment models, ultimately guiding intervention to prevent disease progression. As such, metabolic profiling represents a vital element in future initiatives to define the human exposome. In the longer term, the methods employed in the present Cd exposure study could be used on a broad scale for baseline surveys in health impact assessments for development projects, going on to play a central role in monitoring populations living near point sources of pollution such as industrial sites.
The late Lars Järup MD, PhD was involved in initiating the study and his contributions were instrumental to its success.
Scientific Committee on Toxicity, Ecotoxicity and the Environment
Food and Agriculture Organization
free induction decay
Joint FAO/WHO Expert Committee on Food Additives
multiple linear regression
nuclear magnetic resonance
partial least squares
reactive oxygen species
standard error of the mean
3-trimethylsilyl-1-[2, 2, 3, 3, -2H4] propionate
This work was funded by the NERC Environment and Human Health programme under project grant (NE/E00895X/1) and further supported by the ECMC (C325/A7241), the CEFIC Long Range Initiative Award for Innovative Science 2009 (recipient HCK), the EU-FP6 CarcinoGenomics project (PL037712), the EU-FP7 Envirogenomarkers Project (226756) and MRC-HPA Centre for Environment and Health. The authors would also like to thank Professor Jeremy K Nicholson for valuable discussion during the preparation of this manuscript.
- Wild CP: Environmental exposure measurement in cancer epidemiology. Mutagenesis. 2009, 24: 117-125.PubMed CentralView ArticlePubMedGoogle Scholar
- Vineis P, Perera F: Molecular epidemiology and biomarkers in etiologic cancer research: the new in light of the old. Cancer Epidemiol Biomarkers Prev. 2007, 16: 1954-1965. 10.1158/1055-9965.EPI-07-0457.View ArticlePubMedGoogle Scholar
- Chadeau-Hyam M, Athersuch TJ, Keun HC, De IM, Ebbels TM, Jenab M, Sacerdote C, Bruce SJ, Holmes E, Vineis P: Meeting-in-the-middle using metabolic profiling - a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers. 2011, 16: 83-88. 10.3109/1354750X.2010.533285.View ArticlePubMedGoogle Scholar
- Rappaport SM, Smith MT: Epidemiology. Environment and disease risks. Science. 2010, 330: 460-461. 10.1126/science.1192603.View ArticlePubMedGoogle Scholar
- Wild CP: Complementing the genome with an "exposome": the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 2005, 14: 1847-1850. 10.1158/1055-9965.EPI-05-0456.View ArticlePubMedGoogle Scholar
- Nicholson JK, Lindon JC, Holmes E: 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999, 29: 1181-1189. 10.1080/004982599238047.View ArticlePubMedGoogle Scholar
- Nicholson JK, Connelly J, Lindon JC, Holmes E: Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov. 2002, 1: 153-161. 10.1038/nrd728.View ArticlePubMedGoogle Scholar
- Keun HC: Metabonomic modeling of drug toxicity. Pharmacol Ther. 2006, 109: 92-106. 10.1016/j.pharmthera.2005.06.008.View ArticlePubMedGoogle Scholar
- Coen M, Holmes E, Lindon JC, Nicholson JK: NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chem Res Toxicol. 2008, 21: 9-27. 10.1021/tx700335d.View ArticlePubMedGoogle Scholar
- Holmes E, Loo RL, Stamler J, Bictash M, Yap IK, Chan Q, Ebbels T, De IM, Brown IJ, Veselkov KA, Daviglus ML, Kesteloot H, Ueshima H, Zhao L, Nicholson JK, Elliott P: Human metabolic phenotype diversity and its association with diet and blood pressure. Nature. 2008, 453: 396-400. 10.1038/nature06882.View ArticlePubMedGoogle Scholar
- Spurgeon DJ, Lawlor A, Hooper HL, Wadsworth R, Svendsen C, Thomas LD, Ellis JK, Bundy JG, Keun HC, Jarup L: Outdoor and indoor cadmium distributions near an abandoned smelting works and their relations to human exposure. Environ Pollut. 2011, 159: 3425-3432. 10.1016/j.envpol.2011.08.028.View ArticlePubMedGoogle Scholar
- Nriagu JO, Pacyna JM: Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature. 1988, 333: 134-139. 10.1038/333134a0.View ArticlePubMedGoogle Scholar
- Nriagu JO: A silent epidemic of environmental metal poisoning?. Environ Pollut. 1988, 50: 139-161. 10.1016/0269-7491(88)90189-3.View ArticlePubMedGoogle Scholar
- Jarup L, Berglund M, Elinder CG, Nordberg G, Vahter M: Health effects of cadmium exposure--a review of the literature and a risk estimate. Scand J Work Environ Health. 1998, 24 (Suppl 1): 1-51.PubMedGoogle Scholar
- Jarup L, Akesson A: Current status of cadmium as an environmental health problem. Toxicol Appl Pharmacol. 2009, 238: 201-208. 10.1016/j.taap.2009.04.020.View ArticlePubMedGoogle Scholar
- Jarup L, Hellstrom L, Alfven T, Carlsson MD, Grubb A, Persson B, Pettersson C, Spang G, Schutz A, Elinder CG: Low level exposure to cadmium and early kidney damage: the OSCAR study. Occup Environ Med. 2000, 57: 668-672. 10.1136/oem.57.10.668.PubMed CentralView ArticlePubMedGoogle Scholar
- Akesson A, Lundh T, Vahter M, Bjellerup P, Lidfeldt J, Nerbrand C, Samsioe G, Stromberg U, Skerfving S: Tubular and glomerular kidney effects in Swedish women with low environmental cadmium exposure. Environ Health Perspect. 2005, 113: 1627-1631. 10.1289/ehp.8033.PubMed CentralView ArticlePubMedGoogle Scholar
- Elinder CG, Edling C, Lindberg E, Kagedal B, Vesterberg O: Assessment of renal function in workers previously exposed to cadmium. Br J Ind Med. 1985, 42: 754-760.PubMed CentralPubMedGoogle Scholar
- Elinder CG, Edling C, Lindberg E, Kagedal B, Vesterberg O: beta 2-Microglobulinuria among workers previously exposed to cadmium: follow-up and dose-response analyses. Am J Ind Med. 1985, 8: 553-564. 10.1002/ajim.4700080607.View ArticlePubMedGoogle Scholar
- Hellstrom L, Elinder CG, Dahlberg B, Lundberg M, Jarup L, Persson B, Axelson O: Cadmium exposure and end-stage renal disease. Am J Kidney Dis. 2001, 38: 1001-1008. 10.1053/ajkd.2001.28589.View ArticlePubMedGoogle Scholar
- Waalkes MP, Rehm S, Cherian MG: Repeated cadmium exposures enhance the malignant progression of ensuing tumors in rats. Toxicol Sci. 2000, 54: 110-120. 10.1093/toxsci/54.1.110.View ArticlePubMedGoogle Scholar
- Huff J, Lunn RM, Waalkes MP, Tomatis L, Infante PF: Cadmium-induced cancers in animals and in humans. Int J Occup Environ Health. 2007, 13: 202-212.PubMed CentralView ArticlePubMedGoogle Scholar
- International Agency for Research on Cancer (IARC): Monographs on the Evaluation of the Carcinogenic Risks to Humans. Beryllium, Cadmium, Mercury and Exposures in the Glass Manufacturing Industry. IARC Scientific Publications. 1993, 119-238.Google Scholar
- U.S. Department of Health and Human Services: National Toxiclogy Program. Tenth Report on Carcinogens. 2000, Research Triangle Park, NCGoogle Scholar
- Johnson MD, Kenney N, Stoica A, Hilakivi-Clarke L, Singh B, Chepko G, Clarke R, Sholler PF, Lirio AA, Foss C, Reiter R, Trock B, Paik S, Martin MB: Cadmium mimics the in vivo effects of estrogen in the uterus and mammary gland. Nat Med. 2003, 9: 1081-1084. 10.1038/nm902.View ArticlePubMedGoogle Scholar
- Liu Z, Yu X, Shaikh ZA: Rapid activation of ERK1/2 and AKT in human breast cancer cells by cadmium. Toxicol Appl Pharmacol. 2008, 228: 286-294. 10.1016/j.taap.2007.12.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Stoica A, Katzenellenbogen BS, Martin MB: Activation of estrogen receptor-alpha by the heavy metal cadmium. Mol Endocrinol. 2000, 14: 545-553. 10.1210/me.14.4.545.PubMedGoogle Scholar
- Jin YH, Clark AB, Slebos RJ, Al-Refai H, Taylor JA, Kunkel TA, Resnick MA, Gordenin DA: Cadmium is a mutagen that acts by inhibiting mismatch repair. Nat Genet. 2003, 34: 326-329. 10.1038/ng1172.PubMed CentralView ArticlePubMedGoogle Scholar
- Jiang G, Xu L, Song S, Zhu C, Wu Q, Zhang L, Wu L: Effects of long-term low-dose cadmium exposure on genomic DNA methylation in human embryo lung fibroblast cells. Toxicology. 2008, 244: 49-55. 10.1016/j.tox.2007.10.028.View ArticlePubMedGoogle Scholar
- Benbrahim-Tallaa L, Waterland RA, Dill AL, Webber MM, Waalkes MP: Tumor suppressor gene inactivation during cadmium-induced malignant transformation of human prostate cells correlates with overexpression of de novo DNA methyltransferase. Environ Health Perspect. 2007, 115: 1454-1459.PubMed CentralPubMedGoogle Scholar
- Oh SH, Lim SC: A rapid and transient ROS generation by cadmium triggers apoptosis via caspase-dependent pathway in HepG2 cells and this is inhibited through N-acetylcysteine-mediated catalase upregulation. Toxicol Appl Pharmacol. 2006, 212: 212-223. 10.1016/j.taap.2005.07.018.View ArticlePubMedGoogle Scholar
- Godt J, Scheidig F, Grosse-Siestrup C, Esche V, Brandenburg P, Reich A, Groneberg DA: The toxicity of cadmium and resulting hazards for human health. J Occup Med Toxicol. 2006, 1: 22-10.1186/1745-6673-1-22.PubMed CentralView ArticlePubMedGoogle Scholar
- Griffin JL, Walker LA, Troke J, Osborn D, Shore RF, Nicholson JK: The initial pathogenesis of cadmium induced renal toxicity. FEBS Lett. 2000, 478: 147-150. 10.1016/S0014-5793(00)01843-3.View ArticlePubMedGoogle Scholar
- Griffin JL, Walker LA, Shore RF, Nicholson JK: Metabolic profiling of chronic cadmium exposure in the rat. Chem Res Toxicol. 2001, 14: 1428-1434. 10.1021/tx015521u.View ArticlePubMedGoogle Scholar
- Nicholson JK, Higham DP, Timbrell JA, Sadler PJ: Quantitative high resolution 1H NMR urinalysis studies on the biochemical effects of cadmium in the rat. Mol Pharmacol. 1989, 36: 398-404.PubMedGoogle Scholar
- Nicholson JK, Kendall MD, Osborn D: Cadmium and mercury nephrotoxicity. Nature. 1983, 304: 633-635. 10.1038/304633a0.View ArticlePubMedGoogle Scholar
- Thomas LD, Hodgson S, Nieuwenhuijsen M, Jarup L: Early kidney damage in a population exposed to cadmium and other heavy metals. Environ Health Perspect. 2009, 117: 181-184.PubMed CentralView ArticlePubMedGoogle Scholar
- Hwang TL, Shaka AJ: Water suppression that works - excitation sculpting using arbitrary wave-forms and pulsed-field gradients. J Magn Reson Series A. 1995, 112: 275-279. 10.1006/jmra.1995.1047.View ArticleGoogle Scholar
- Fan TWM: Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Prog Nucl Magn Reson Spectrosc. 1996, 28: 161-219.View ArticleGoogle Scholar
- Cloarec O, Dumas ME, Craig A, Barton RH, Trygg J, Hudson J, Blancher C, Gauguier D, Lindon JC, Holmes E, Nicholson J: Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal Chem. 2005, 77: 1282-1289. 10.1021/ac048630x.View ArticlePubMedGoogle Scholar
- Appiah-Amponsah E, Shanaiah N, Nagana Gowda GA, Owusu-Sarfo K, Ye T, Raftery D: Identification of 4-deoxythreonic acid present in human urine using HPLC and NMR techniques. J Pharm Biomed Anal. 2009, 50: 878-885. 10.1016/j.jpba.2009.06.007.PubMed CentralView ArticlePubMedGoogle Scholar
- Dieterle F, Ross A, Schlotterbeck G, Senn H: Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006, 78: 4281-4290. 10.1021/ac051632c.View ArticlePubMedGoogle Scholar
- Teichert F, Verschoyle RD, Greaves P, Thorpe JF, Mellon JK, Steward WP, Farmer PB, Gescher AJ, Singh R: Determination of 8-oxo-2'-deoxyguanosine and creatinine in murine and human urine by liquid chromatography/tandem mass spectrometry: application to chemoprevention studies. Rapid Commun Mass Spectrom. 2009, 23: 258-266. 10.1002/rcm.3873.View ArticlePubMedGoogle Scholar
- Shih CM, Ko WC, Wu JS, Wei YH, Wang LF, Chang EE, Lo TY, Cheng HH, Chen CT: Mediating of caspase-independent apoptosis by cadmium through the mitochondria-ROS pathway in MRC-5 fibroblasts. J Cell Biochem. 2004, 91: 384-397. 10.1002/jcb.10761.View ArticlePubMedGoogle Scholar
- Badisa VL, Latinwo LM, Odewumi CO, Ikediobi CO, Badisa RB, Ayuk-Takem LT, Nwoga J, West J: Mechanism of DNA damage by cadmium and interplay of antioxidant enzymes and agents. Environ Toxicol. 2007, 22: 144-151. 10.1002/tox.20248.View ArticlePubMedGoogle Scholar
- Evans MD, Dizdaroglu M, Cooke MS: Oxidative DNA damage and disease: induction, repair and significance. Mutat Res. 2004, 567: 1-61. 10.1016/j.mrrev.2003.11.001.View ArticlePubMedGoogle Scholar
- Loft S, Hogh DP, Mikkelsen L, Risom L, Forchhammer L, Moller P: Biomarkers of oxidative damage to DNA and repair. Biochem Soc Trans. 2008, 36: 1071-1076. 10.1042/BST0361071.View ArticlePubMedGoogle Scholar
- CSTEE: Opinion on the Results of the Risk Assessment of: Cadmium Metal Human Health (CAS no.:7440-43-9, EINECS no.:231-152-8) Cadmium Oxide Human Health (CAS no.:1306-19-0, EINECS no.:215-146-2). 2004Google Scholar
- JECFA (FAO/WHO): Evaluation of certain food additives and contaminants WHO food additives series, No. 52. 2004Google Scholar
- Hamm LL: Renal handling of citrate. Kidney Int. 1990, 38: 728-735. 10.1038/ki.1990.265.View ArticlePubMedGoogle Scholar
- Cardellach F, Alonso JR, Lopez S, Casademont J, Miro O: Effect of smoking cessation on mitochondrial respiratory chain function. J Toxicol Clin Toxicol. 2003, 41: 223-228. 10.1081/CLT-120021102.View ArticlePubMedGoogle Scholar
- Miro O, Alonso JR, Jarreta D, Casademont J, Urbano-Marquez A, Cardellach F: Smoking disturbs mitochondrial respiratory chain function and enhances lipid peroxidation on human circulating lymphocytes. Carcinogenesis. 1999, 20: 1331-1336. 10.1093/carcin/20.7.1331.View ArticlePubMedGoogle Scholar
- Chalkley SR, Richmond J, Barltrop D: Measurement of vitamin D3 metabolites in smelter workers exposed to lead and cadmium. Occup Environ Med. 1998, 55: 446-452. 10.1136/oem.55.7.446.PubMed CentralView ArticlePubMedGoogle Scholar
- Belyaeva EA, Dymkowska D, Wieckowski MR, Wojtczak L: Mitochondria as an important target in heavy metal toxicity in rat hepatoma AS-30D cells. Toxicol Appl Pharmacol. 2008, 231: 34-42. 10.1016/j.taap.2008.03.017.View ArticlePubMedGoogle Scholar
- Ballevre O, Prugnaud J, Houlier ML, Arnal M: Assessment of threonine metabolism in vivo by gas chromatography/mass spectrometry and stable isotope infusion. Anal Biochem. 1991, 193: 212-219. 10.1016/0003-2697(91)90011-H.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1741-7015/10/61/prepub
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