Urine cadmium levels and albuminuria in a general population from Spain: A gene-environment interaction analysis

Environment International 106 (2017) 27–36

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Urine cadmium levels and albuminuria in a general population from Spain: A gene-environment interaction analysis

MARK

Maria Grau-Pereza,b,1, Gernot Pichlerb,j,1, Inma Galan-Chiletc, Laisa S. Briongos-Figuerod, Pilar Rentero-Garridoc, Raul Lopez-Izquierdod, Ana Navas-Aciena,e, Virginia Weavere, Tamara García-Barreraf,g, Jose L. Gomez-Arizaf,g, Juan C. Martín-Escuderod, F. Javier Chavesc,h, Josep Redonb,i,j, Maria Tellez-Plazab,e,⁎ a

Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA Area of Cardiometabolic and Renal Risk, Institute for Biomedical Research INCLIVA, Valencia, Spain Genotyping and Genetic Diagnosis Unit, Institute for Biomedical Research INCLIVA, Valencia, Spain d Department of Internal Medicine, University Hospital Rio Hortega, Valladolid, Spain e Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA f Department of Chemistry, Faculty of Experimental Science, University of Huelva, Huelva, Spain g Research Center of Health and Environment (CYSMA), University of Huelva, Huelva, Spain h CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Barcelona, Spain i CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Institute of Health Carlos III, Minister of Health, Madrid, Spain j Department of Internal Medicine, Hospital Clínico de Valencia, University of Valencia, Spain b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Urine cadmium Albuminuria Gene-environment interaction Population-based survey

Background: The interaction of cadmium with genes involved in oxidative stress, cadmium metabolism and transport pathways on albuminuria can provide biological insight on the relationship between cadmium and albuminuria at low exposure levels. Objectives: We tested the hypothesis that specific genotypes in candidate genes may confer increased susceptibility to cadmium exposure. Methods: Cadmium exposure was estimated by inductively coupled plasma mass spectrometry (ICPMS) in urine from 1397 men and women aged 18–85 years participating in the Hortega Study, a representative sample of a general population from Spain. Urine albumin was measured by automated nephelometric immunochemistry. Abnormal albuminuria was defined as urine albumin greater than or equal to 30 mg/g. Results: The weighted prevalence of abnormal albuminuria was 6.3%. The median level of urine cadmium was 0.39 (IQR, 0.23–0.65) μg/g creatinine. Multivariable-adjusted geometric mean ratios of albuminuria comparing the two highest to the lowest tertile of urine cadmium were 1.62 (95% CI, 1.43–1.84) and 2.94 (95% CI, 2.58–3.35), respectively. The corresponding odds ratios of abnormal albuminuria were 1.58 (0.83, 3.02) and 4.54 (2.58, 8.00). The association between urine cadmium and albuminuria was observed across all participant subgroups evaluated including participants without hypertension, diabetes or chronic kidney disease. We observed Bonferroni-corrected statistically significant interactions between urine cadmium levels and polymorphisms in gene SLC30A7 and RAC1. Conclusions: Increasing urine cadmium concentrations were cross-sectionally associated with increased albuminuria in a representative sample of a general population from Spain. Genetic variation in oxidative stress and cadmium metabolism and transport genes may confer differential susceptibility to potential cadmium effects.

1. Introduction Cadmium exposure is widespread, as cadmium can be found in tobacco smoke, some foods (green and root vegetables, grains, shellfish

⁎

1

and organ meats) and ambient air (Nordberg et al., 2007). In the general population, the main routes of cadmium exposure include the active or passive inhalation of tobacco smoke, and the oral ingestion of contaminated food and drinking water (US Department of Health and

Corresponding author at: Institute for Biomedical Research Hospital Clinic de Valencia (INCLIVA), Av. Menendez Pelayo 4 accesorio, 46010 Valencia, Spain. E-mail addresses: [email protected], [email protected] (M. Tellez-Plaza). Co-first authors.

http://dx.doi.org/10.1016/j.envint.2017.05.008 Received 5 January 2017; Received in revised form 7 April 2017; Accepted 10 May 2017 0160-4120/ © 2017 Elsevier Ltd. All rights reserved.

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age-specific strata were selected among individuals who responded to the initial phase of the study to undergo interview and clinical examination and to provide biological samples. In order to guarantee the collection of reliable information participants with serious concomitant diseases or disorders and with mental or social conditions that could complicate or prevent participation in the study were excluded. No exclusions were explicitly made based on kidney disease status of participants. After signing an informed consent form, biological samples were collected and stored, resulting in 1502 participants with available urine for metal determination. 18 participants were excluded due to missing urine cadmium measurements, 4 participants due to missing albumin measurements and 83 participants due to missing other relevant covariates, leaving 1397 participants for this study. The research protocol was approved by ethical committee of the Rio Hortega University Hospital of Valladolid.

Human Services, 2012). Cadmium is a well-established carcinogen and nephrotoxicant (Nordberg et al., 2007; Roels et al., 1989). At high exposure level, cadmium is associated with impaired tubular reabsorption and proteinuria, proximal tubular atrophy, interstitial fibrosis and renal vascular changes (Maruzeni et al., 2014; Nishijo et al., 2006; Nogawa et al., 2004; Prozialeck et al., 2008; Uetani et al., 2007; Yasuda et al., 1995). At low cadmium exposure levels, the association between cadmium and markers of kidney disease is not fully understood. For instance, because cadmium binds to various proteins in serum, it has been argued that urine cadmium concentrations can be related to physiological protein excretion through the glomerulus (Akerstrom et al., 2013; Bernard, 2008). Abnormal albuminuria concentrations, defined as urine concentrations of albumin greater than or equal to 30 mg/g, can reflect either increased albumin excretion through the glomeruli due to increased endothelial permeability or decreased albumin reabsorption in proximal tubules (Fassett et al., 2011; Redon et al., 2015). Albuminuria is a well-established marker of kidney damage in diabetes and hypertension (Lopez-Giacoman and Madero, 2015). Other causes of albuminuria include primary glomerular disease and kidney damage secondary to systemic diseases. However, these conditions are relatively rare in a population-based setting (McGrogan et al., 2011; Wetmore et al., 2016). In addition, albuminuria is considered as an overall marker of endothelial damage and has been positively associated with increased mortality in several populations (Chronic Kidney Disease Prognosis Consortium et al., 2010; Matsushita et al., 2015; Nitsch et al., 2013; Xu et al., 2007). Few epidemiologic studies have evaluated the association of cadmium with albuminuria at low exposure levels. In non-occupationally exposed populations from the US (geometric mean urinary cadmium 0.22 μg/L (Buser et al., 2016) and geometric mean blood cadmium 0.41 μg/L (Navas-Acien et al., 2009)), China (median urinary cadmium excretion 2.25 μg/L) (Zhang et al., 2015) and Australia (geometric mean urinary cadmium 0.83 μg/g creatinine) (HaswellElkins et al., 2008), increasing cadmium levels were consistently associated with increasing albuminuria. Nonetheless, population-based studies from Europe are scarce. Mechanistic and epidemiologic studies suggest a role of cadmium in altering the redox balance (Jomova and Valko, 2011; Valko et al., 2016). In turn, oxidative stress conditions may promote cadmium toxicity in the endothelium and the kidney. Studies evaluating the interaction of cadmium and genetic variation in genes involved in oxidative stress and cadmium metabolism and transport pathways on albuminuria, however, are scarce. Such gene-environment interaction studies can provide etiological insight into cadmium-associated albuminuria as significant interactions may potentially point to common or inter-related biological pathways. Our objective was, thus, to evaluate the cross-sectional association between urine cadmium and albuminuria in a representative sample of the general population from Valladolid (Spain), and to test the hypothesis that specific genotypes in candidate genes may confer increased susceptibility to cadmium exposure.

2.2. Urine cadmium levels Urine cadmium levels were measured by inductively coupled plasma mass spectrometry with dynamic reaction cell on an Agilent 7500CEx ICP-OR-MS (Agilent Technologies, United States) following a standardized protocol in the Environmental Bioanalytical Chemistry (AMB) Laboratory at Huelva University (Spain). The lower detection limit for urine cadmium levels was 0.001 μg/L. In the present study, no individual had levels below the detection limit. The intra-assay and inter-assay coefficient variation were 5.2% and 7.2%, respectively.

2.3. Albumin levels Urine albumin was measured by automated nephelometric immunochemistry (Behring Institute). The limit of detection for urinary albumin was 2.3 mg/L and a total of 471 participants (33.7%) were below the limit of detection. For participants with albuminuria levels below the limit of detection, a concentration equal to the limit of detection divided by the squared root of 2 was imputed (Hornung et al., 1996). The intra-assay and inter-assay coefficient of variation for urine albumin measurement in our laboratory was 2% and 6%, respectively. The ratio of urinary albumin to urinary creatinine (ACR) was reported in milligrams per gram. We defined abnormal albuminuria as an ACR greater than or equal to 30 mg/g.

2.4. Other variables Information on age, sex, education, smoking status, cumulative exposure to active tobacco smoke (measured as pack-years) and alcohol consumption was based on self-report (Escudero et al., 2003). Body mass index (BMI) was calculated dividing measured weight in kilograms by measured height in meters squared. Urine cotinine was measured by enzyme-linked immunosorbent assay (ELISA) (Kit “Análisis DRI® Cotinina”, Ref. 0395 Microgenics laboratories), with a limit of detection of 34 ng/mL (77% of participants below the limit of detection). Participants were considered to have diabetes mellitus if the level of fasting glucose was 126 mg/dL or higher, if hemoglobin A1c was 6.5% or higher, if they had been previously diagnosed of type 2 diabetes by a physician or if they had a record of use of diabetes medications in the clinical history. Blood pressure was measured using a mercury sphygmomanometer. Systolic BP (SBP) and diastolic BP (DBP) were the average of 3 readings measured at 5-min intervals. Urine and serum creatinine were measured by the modified kinetic Jaffé method by isotope dilution mass spectrometry (IDMS) on a Hitachi 917 analyzer (Roche Diagnostics GmbH, Mannheim Germany). The glomerular filtration rate was estimated based on serum creatinine determinations (eGFR) by the CKD-EPI abbreviated formula (Levey et al., 2009).

2. Methods 2.1. Study population The Hortega Study is a population-based survey carried out from 1997 to 2003 in adults 15–85 years old assigned to the Rio Hortega University Hospital's health care area in Valladolid (Spain). Participants were selected based on a list of beneficiaries from the universal health care system, which provided a comprehensive representation of the individuals living in the study area (Mena-Martin et al., 2003). In a first step, 20% of the 179,600 individuals included in the registry were randomly selected and invited by mail to participate in the study. The invitation included a questionnaire to collect preliminary information of cardiovascular history and risk factors. The response rate in this step was 33%. In a second step, ~250 participants from each of 6 sex and 28

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statistically significant SNP-cadmium interactions, we reported the associations of cadmium and albumin among subgroups of participants with the genotypes of interest in this specific model. If > 1 inheritance model showed statistically significant SNP-cadmium interactions, we reported the best fitting inheritance model that was selected by comparing the associations estimated from a general model that included separate dummy variables for the heterozygote and minor allele homozygote (reference major allele homozygote) with the associations obtained assuming the dominant (minor allele homozygote and heterozygote versus major allele homozygote), recessive (minor allele homozygote versus heterozygote and major allele homozygote) and additive models (0, 1, or 2, minor allele dosage).

2.5. DNA isolation, SNP selection and genotyping DNA was isolated from peripheral blood cells using Chemagic System (Chemagen), and quality assessment was performed with PicoGreen dsDNA Quantification Reagent (Invitrogen, Carlsbad, CA, USA). DNA was diluted to a final concentration of 100 ng/μL. 524 single nucleotide polymorphisms (SNPs) from 133 candidate genes (genes coding for proteins involved in redox and mitochondrial respiratory chain reactions and other biological pathways directly or indirectly related to oxidative stress, cadmium metabolism and transport and albuminuria) were identified by bibliography search and using the SYSNPS program (Lorente-Galdos et al., 2012). We included SNPs previously reported to be related to albuminuria in meta-analyses and GWAS in humans or to have functional implications for nucleotide change. The SNPs were genotyped using an oligo-ligation-assay (SNPlex, Applied Biosystems, Foster City) following the manufacturer's protocol. The polymorphisms nomenclature was based on recommendations by den Dunnen and Antonarakis (2001). We excluded 35 SNPs because they were genotyped in less than the 90% of the study sample, 58 SNPs because they did not have 3 genotypes, 29 SNPs because they had a minor allele frequency less than the 1%, 35 SNPs because they did not meet Hardy–Weinberg equilibrium (p-value < 0.01) and 61 because the frequency of minor genotype was below 20, leaving 306 SNPs to be included in gene-environment interaction analyses. The mean genotyping coverage across all the included genotyped SNPs was 96.9%.

3. Results The geometric mean of urine cadmium levels in the study was 0.38 μg/g. Urine cadmium levels were higher in older individuals, males, smokers, and participants with higher levels of cotinine or smoked cigarette packs-years. Among never-smokers, urine cadmium levels were higher in women compared to men. We found no significant differences according to education, body mass index, alcohol intake, hypertension, diabetes and glomerular filtration rate (Table 1). The geometric mean of albuminuria levels was 4.8 mg/g creatinine (95% CI 4.5, 5.1). The weighted prevalence of abnormal albuminuria was 6.3%. Exposure to cadmium was positively associated with urine albumin concentrations (Table 2 and Supplemental Fig. 1). Fully adjusted geometric mean ratios (GMRs) (95%CI) of urine albumin concentrations comparing the second and third tertiles to the lowest tertile of urine cadmium levels were 1.62 (1.43, 1.84) and 2.94 (2.58, 3.35), respectively (Table 2). The GMR comparing the 80th (0.76 μg/g) to the 20th (0.19 μg/g) percentile of cadmium distribution was 2.07 (1.88, 2.27) (p-value for lineal trend < 0.001) (Table 2). The corresponding odds ratios (ORs) for abnormal albuminuria were 1.58 (0.83, 3.02), 4.54 (2.58, 8.00) for the tertiles models and 3.02 (2.02, 4.51) (p-lineal trend < 0.001) for models comparing the 80th to the 20th percentile of cadmium distribution (Table 3). In previous epidemiological studies, sex-specific cut-offs for the definition of abnormal albuminuria have been used (Dyer et al., 2004; Jacobs et al., 2002; Murtaugh et al., 2003). Sensitivity analysis defining cut-off values for abnormal albuminuria of 20 mg/g in men and 30 mg/ g in women showed similar findings (Supplemental Table 1). The results were consistent in all subgroups evaluated (Fig. 1, Supplemental Fig. 1). In abnormal albuminuria models, ever smokers showed somewhat stronger associations (interaction p-value = 0.04) (Fig. 1, Supplemental Fig. 1). In addition, individuals with reduced glomerular filtration rate showed weaker associations between cadmium and abnormal albuminuria (interaction p-value < 0.001) (Fig. 1, Supplemental Fig. 1). In sensitivity analysis, cadmium not divided by creatinine was entered into the regression models with and without adjustment for urinary creatinine, with somewhat attenuated, although consistent and statistically significant, associations (data not shown). Table 4 shows the association of cadmium and albuminuria among genotypes of polymorphisms in genes with the top 10 p-values for geneenvironment interaction. In continuous urine albumin models, we found a Bonferroni-corrected statistically significant interaction between urine cadmium levels and the SNPs rs3087816 in gene SLC30A7 (1.97 [1.81, 2.14] and 5.10 [3.35, 7.76] for carriers of the TT + TC and CC genotypes, respectively) and rs4720672 in gene RAC1 (1.88 [1.71, 2.06] and 2.66 [2.28, 3.11], for carriers of the TT and TC + CC genotypes, respectively) (Fig. 2, Table 4). While abnormal albuminuria recessive models showed interactions at the Bonferroni-corrected level of statistical significance for rs3087816 in SLC30A7 and rs9610684 in RAC2, the number of cases in the recessive genotype was low making the estimation of differential

2.6. Statistical methods Statistical analyses were weighted to the underlying population in the catchment area of the Rio Hortega's University Hospital. For data analysis, creatinine corrected urine cadmium and albumin levels were markedly right-skewed and log-transformed. Cut-offs for urine cadmium tertiles were based on weighted distributions in the study sample. We assessed the association of urine cadmium levels with albuminuria levels using linear regression models and with abnormal albuminuria using logistic regression. We introduced cadmium in the models in two ways: 1) comparing each of two highest tertiles of urine cadmium with the lowest tertile; and 2) comparing 80th and 20th urine cadmium percentiles (i.e. for an interquintile change increase). Regression models were fitted with increasing degrees of adjustment. Initially, model 1 was adjusted for age, sex, education (< high school, ≥ high school) and body mass index. Model 2 was further adjusted for smoking status (never, former and current), cumulative smoking dose (packyears), urine cotinine (< 34, 34–500 and > 500 mg/dL) and alcohol intake (mg/day). Model 3 was additionally adjusted for hypertension (yes, no), diabetes (yes, no) and eGFR (mL/min/1.73m2). In secondary analyses, we also graphically displayed the geometric mean ratios of albuminuria and the odds ratios of abnormal albuminuria based on restricted quadratic splines with knots at the 10th, 50th and 90th percentiles of the urine cadmium distribution, including interaction terms for log-transformed urine cadmium concentration with indicator variables for subgroups defined by sex (men, women), ever smoking (never, ever), hypertension (no, yes) and diabetes (no, yes), in separate models. P-values for trend and interaction were obtained from Wald tests. Gene-environment interaction analyses were conducted by including interaction terms for log-transformed urine cadmium with indicator variables for genotypes. For each SNP we obtained three p-values for the interaction with cadmium assuming, respectively, dominant, recessive and additive inheritance in separate models using F-tests of global significance comparing nested models with and without the corresponding interaction terms. We estimated a Bonferroni-corrected alpha level of statistical significance equal to 0.0002 (0.05 divided by an estimated effective number of SNPs equal to 217 based on linkage disequilibrium). In cases where only 1 inheritance model showed 29

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Table 1 Participants' characteristics by urine cadmium levels. Urine cadmium (μg/g)

N (participants) Age (years), mean (SE) a Gender, % of males (SE) Education, % of < secondary education (SE) Body Mass Index (kg/m2), mean (SE) Smoking status, % (SE) Never Former Current Urine cotinine (mg/day), GM (95% CI) Cumulative (cigarette pack-year), mean (SE) Alcohol (mg/day), mean (SE) Alcohol status (mg/day), % (SE) 0 0–2.2 > 2.2 Hypertension, % (SE) Type 2 diabetes, % (SE) eGFR (mL/min/1.73m2), mean (SE) eGFR (mL/min/1.73m2), % of < 60 (SE) Albuminuria (mg/g creatinine), GM (95% CI) Abnormal albuminuria (≥ 30 mg/g), % (SE)

Overall

Tertile 1 (< = 0.27)

Tertile 2 (0.27–0.54)

Tertile 3 (> 0.54)

1397 49.7 (0.2) 49.0 (0.0) 22.1 (1.0) 26.1 (0.1)

461 48.6 43.1 21.3 25.9

465 49.5 50.9 23.3 26.1

471 50.9 52.9 21.8 26.4

(0.6) (1.9) (1.8) (0.2)

0.05 < 0.001 0.33 0.21

44.5 (1.3) 28.2 (1.2) 27.3 (1.2) 8.2 (6.7, 9.9) 9.2 (0.5) 11.1 (0.6)

54.0 (2.4) 24.9 (2.1) 21.1 (2.0) 6.0 (5.0, 7.4) 6.3 (1.0) 11.1 (1.0)

43.2 (2.3) 28.8 (2.1) 28.1 (2.2) 7.4 (6.1, 9.0) 9.1 (0.8) 10.7 (1.0)

36.6 30.9 32.5 13.5 12.2 11.7

(2.2) (2.2) (2.3) (9.1, 19.9) (0.7) (1.0)

< 0.001 0.005 < 0.001 < 0.001 < 0.001 0.52

38.0 (1.2) 9.4 (0.8) 52.6 (1.3) 35.8 (1.2) 5.6 (0.6) 93.9 (0.4) 7.0 (0.6) 4.8 (4.5, 5.1) 6.3 (0.6)

37.2 (2.2) 9.4 (1.4) 53.4 (2.3) 33.5 (2.1) 6.0 (1.0) 93.5 (0.9) 8.0 (1.2) 2.9 (2.7, 3.2) 3.1 (0.7)

39.7 (2.3) 9.5 (1.4) 50.8 (2.3) 35.6 (2.2) 4.8 (0.9) 93.6 (0.9) 7.0 (1.1) 4.6 (4.2, 5.1) 4.7 (1.0)

37.1 (2.2) 9.2 (1.4) 53.7 (2.3) 38.4 (2.2) 6.1 (1.1) 94.6 (0.8) 6.2 (0.9) 8.1 (7.3, 9.0) 11.1 (1.4)

(0.6) (1.9) (1.8) (0.2)

(0.6) (2.0) (1.9) (0.2)

P Lineal trend

0.73 0.74 0.60 0.24 0.64 0.64 0.83 < 0.001 < 0.001

a For never-smokers, % (SE) of men in overall was 49.0 (0), and in tertiles 1 to 3 were 52.0 (2.8), 47.4 (3.4) and 46.3 (3.9), respectively. Abbreviations: eGFR, estimated glomerular filtration rate; SE, standard error, GM, geometric mean; CI, confidence interval.

Table 2 Geometric mean ratio (95% confidence interval) of albuminuria levels by urine cadmium concentrations. Urine cadmium (μg/g)

Tertile 1 (< = 0.27) Tertile 2 (0.27–0.54) Tertile 3 (> 0.54) 80th to 20th percentile P lineal trend

Table 3 Odds ratio (95% confidence interval) of abnormal albuminuria by urine cadmium concentrations.

Albuminuria, mg/g

Urine cadmium (μg/g)

Model 1 GMR (95% CI)

Model 2 GMR (95% CI)

Model 3 GMR (95% CI)

1 (Reference) 1.61 (1.41, 1.83) 2.86 (2.50, 3.28) 2.04 (1.85, 2.25) < 0.001

1 (Reference) 1.60 (1.40, 1.82) 2.86 (2.50, 3.27) 2.04 (1.85, 2.25) < 0.001

1 (Reference) 1.62 (1.43, 1.84) 2.94 (2.58, 3.35) 2.07 (1.88, 2.27) < 0.001

Tertile 1 (< = 0.27) Tertile 2 (0.27–0.54) Tertile 3 (> 0.54) 80th to 20th percentile P lineal trend

Model 1 adjusted for age (years, splines), gender (male, female), education (< high school, > = high school) and body mass index (kg/m2). Model 2 further adjusted for cotinine (< 34, 34–500, > 500 mg/dL), smoking status (never, former, current), cumulative smoking dose (pack/year) and alcohol intake (mg/ day). Model 3 further adjusted for hypertension (no, yes), diabetes mellitus type 2 (no, yes) and estimated glomerular filtration rate (mL/min/1.73m2). The 80th and 20th percentiles of urine cadmium distribution were 0.76 and 0.19 μg/g, respectively.

Cases/ Noncases

Albuminuria, ≥30 mg/g Model 1 OR (95% CI)

Model 2 OR (95% CI)

Model 3 OR (95% CI)

19/444

1 (Reference)

1 (Reference)

1 (Reference)

27/437

1.61 (0.85, 3.07) 4.16 (2.34, 7.42) 2.78 (1.89, 4.08) < 0.001

1.56 (0.82, 2.98) 4.02 (2.27, 7.12) 2.74 (1.86, 4.02) < 0.001

1.58 (0.83, 3.02) 4.54 (2.58, 8.00) 3.02 (2.02, 4.51) < 0.001

60/410 106/ 1291

Model 1 adjusted for age (years, splines), gender (male, female), education (< high school, > = high school) and body mass index (kg/m2). Model 2 further adjusted for smoking status (never, former, current), cumulative smoking dose (pack/year), cotinine (< 34, 34–500, > 500 mg/dL) and alcohol intake (mg/day). Model 3 further adjusted for hypertension (no, yes), diabetes mellitus type 2 (no, yes) and estimated glomerular filtration rate (mL/min/1.73m2). The 80th and 20th percentiles of urine cadmium distribution were 0.76 and 0.19 μg/g, respectively. We evaluated linearity by using the Wald test for the coefficient corresponding to log-transformed cadmium in the regression models.

associations and interaction p-values difficult (Supplemental Fig. 2, Supplemental Table 2). Additive models for rs3087816 in SLC30A7 and rs3179967 in RAC2, which are more robust, were marginally significant (interaction p-values were 2.9·10− 4 and 2.8·10− 4, respectively).

related to the RAC family, which encode proteins involved in the transport of tubular albumin, and also, in oxidative-stress mediated endothelial damage and dysfunction. Cadmium is a divalent cation that shares metabolic and transport pathways with Zinc and other essential metals. The interaction of cadmium with genes involved in redox pathways was suggestive. These results support that genetic variation may confer differential susceptibility to potential cadmium effects. Chronic exposure to low levels of cadmium is an increasingly recognized concern because cadmium biomarkers at substantially low concentrations have been associated with a number of health effects including cancer (Bishak et al., 2015; Huff et al., 2007; Larsson et al., 2015; Liu et al., 2009; Luevano and Damodaran, 2014), cardiovascular disease (Everett and Frithsen, 2008; Lee and Kim, 2012a; Lee et al.,

4. Discussion In a representative sample of a general population from Spain, increasing urine cadmium concentrations showed a strong linear association with increasing urine albumin concentrations. The observed associations persisted after extensive adjustment for main albuminuria risk factors and remained in all subgroups evaluated including participants without reduced glomerular filtration rate, hypertension or diabetes. In addition, we found strong evidence of effect modification in cadmium-related albuminuria by genotypes of SLC30A7, a gene that encodes proteins involved in the transport of endosomal zinc, and genes 30

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GMR (95% CI)

P for Interaction

Male

1.97 (1.72, 2.27)

0.39

Female

2.15 (1.89, 2.44)

Cases/ Non cases

Albuminuria

OR (95% CI)

P for Abnormal Albuminuria Interaction

Gender 53/649

3.25 (1.85, 5.71)

53/642

2.88 (1.62, 5.10)

48/606

2.00 (1.09, 3.68)

58/685

4.66 (2.77, 7.86)

34/765

4.10 (2.18, 7.71)

72/526

2.18 (1.39, 3.42)

84/1209

3.22 (2.08, 4.98)

22/82

1.97 (0.80, 4.84)

0.77

Smoking Status Never

1.99 (1.76, 2.25)

Ever

2.13 (1.85, 2.46)

0.46

0.04

Hypertension No

2.19 (1.97, 2.42)

Yes

1.89 (1.59, 2.24)

0.15

0.12

Diabetes No

2.03 (1.85, 2.23)

Yes

2.69 (1.59, 4.56)

0.30

0.34

Low eGFR No

2.07 (1.88, 2.28)

75/1181

3.81 (2.39, 6.05)

Yes

1.97 (1.33, 2.93)

0.81

31/110

1.30 (0.78, 2.17)

Overall

2.07 (1.88, 2.27)

106/1291

3.02 (2.02, 4.51)

1.0

1.8

3.1

5.5

<0.001

1.0

1.8

3.1

5.5

Fig. 1. Geometric mean ratio (95% confidence interval) of albuminuria levels and odds ratio (95% confidence interval) of abnormal albuminuria by urine cadmium concentrations and by participant subgroups. The 80th and 20th percentiles of urine cadmium distribution were 0.76 and 0.19 μg/g, respectively. We evaluated interaction by using the Wald test for the coefficient corresponding to the interaction terms in the regression models. Model was adjusted for age (splines), gender (male, female), educational level (< high school, > = high school), body mass index (kg/m2), smoking status (never, former, current), cumulative smoking dose (pack-year), urine cotinine (< 34, 34–500, > 500 mg/dL), alcohol intake (mg/day), hypertension status (no, yes), type 2 diabetes (no, yes) and glomerular filtration rate (mL/min/1.73 m2). The area of each data marker is inversely proportional to the variance of each estimate.

2014; Buser et al., 2016; Järup et al., 2000; Navas-Acien et al., 2009). Available data on low-level cadmium exposure and albuminuria is scarce (Buser et al., 2016; Navas-Acien et al., 2009; Zhang et al., 2015). Recent data from the NHANES 2007–2012 survey (N = 4875, geometric mean urinary cadmium 0.22 μg/L) showed a significant doseresponse relationship of urinary cadmium with the excretion of urinary albumin (Buser et al., 2016). While our findings are consistent with the data from the general US population, additional studies in general populations with low cadmium exposure levels are needed. In circulating blood, cadmium binds to albumin and is transported to the liver, where it binds to glutathione and metallothionein I (MT-I). The cadmium-MT-I complex is filtered by the glomerulus and entirely reabsorbed in the proximal convoluted tubule, where metallothionein is removed releasing free cadmium into the cells (Nordberg et al., 2007). In conditions associated to glomerular damage and increased endothelial permeability, such as diabetes and hypertension, there is an increased leakage of albumin and other proteins into the urine (Coresh et al., 2005). In addition, it is known that in physiological conditions there is also albumin filtration along with a significant tubular reabsorption by receptor-mediated endocytosis and transcytotic retrieval of intact albumin (Pollock and Poronnik, 2007). In the absence of prospective population-based studies in adults, it has been, thus, suggested that increased cadmium excretion due to cadmium-protein binding in situations of increased urinary excretion of proteins may explain findings from cross-sectional studies (Akerstrom et al., 2013; Chaumont et al., 2013). In our study population, urine cadmium was not associated to glomerular filtration rate (difference in glomerular filtration rate comparing the 80th to the 20th percentile of cadmium distribution was 0.82 [95%CI -0.41, 2.05] after adjustment for age, gender, education, BMI, smoking status, cumulative smoking dose, cotinine, hypertension and diabetes), suggesting that the observed associations are unrelated to glomerular function. Indeed, the association between urinary cadmium and albuminuria remained significant after adjusting

Table 4 Geometric mean ratio (95% confidence interval) of albuminuria levels comparing 80th vs. 20th percentiles of cadmium distribution by top 10 polymorphisms. Gene

SNP

Model

Genotype

N

GMR (95% CI)

SLC30A7

rs3087816

REC

SLC40A1

rs1439816

REC

NR3C2

rs2070951

DOM

COX7A2

rs9360898

DOM

RAC1

rs4720672

DOM

rs9374

DOM

rs769217

REC

rs769218

REC

rs1049982

REC

rs2277448

REC

T/T + T/C C/C G/G + G/C C/C C/C C/G + G/G T/T T/G + G/G T/T T/C + C/C G/G G/A + A/A C/C + C/T T/T G/G + G/A A/A C/C + C/T T/T T/T + T/G G/G

1258 50 1253 57 330 964 814 568 988 383 908 474 1279 104 1271 109 1196 195 1224 115

1.97 5.10 2.13 1.35 1.69 2.24 2.32 1.80 1.88 2.66 1.87 2.51 1.99 3.60 1.98 3.41 1.97 2.92 1.95 3.35

CAT

ATP7B

(1.81, (3.35, (1.95, (1.01, (1.44, (2.03, (2.07, (1.60, (1.71, (2.28, (1.69, (2.18, (1.83, (2.59, (1.82, (2.49, (1.81, (2.30, (1.80, (2.43,

P int.

2.14)1.37·10− 5 7.76) 2.32)0.0034 1.81) 1.98)0.0028 2.48) 2.59)0.0023 2.03) 2.06)1.51·10− 4 3.11) 2.06)7.35·10− 4 2.88) 2.16)6.69·10− 4 5.02) 2.15)0.0012 4.68) 2.15)0.0025 3.70) 2.12)0.0014 4.63)

Abbreviations: GMR, geometric mean ratio; CI, confidence interval; REC, recessive model; DOM, dominant model; ADD, additive model. Model was adjusted for age (splines), gender (male, female), educational level (< high school, > = high school), body mass index (kg/m2), smoking status (never, former, current), cumulative smoking dose (pack-year), urine cotinine (< 34, 34–500, > 500 mg/dL), alcohol intake (mg/day), hypertension status (no, yes), type 2 diabetes (no, yes) and glomerular filtration rate (mL/min/1.73m2).

2011; Navas-Acien et al., 2004; Peters et al., 2010; Schwartz et al., 2003; Tellez-Plaza et al., 2008, 2010), bone damage (Engström et al., 2011; Gallagher et al., 2008; James and Meliker, 2013; Kazantzis, 2004) and renal dysfunction (Akesson et al., 2005; Barregard et al.,

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Fig. 2. Candidate genes-cadmium interaction –log10 p-values. P-values for the interaction of cadmium with 306 SNPs derived from linear regression models (dominant, recessive and additive model) for the geometric mean ratio of albuminuria levels adjusted by age, sex, education, body mass index, smoking status, cumulative smoking dose (pack-year), urine cotinine levels (< 34, 34–500 and > 500 ng/mL) and alcohol consumption (mg/day), are presented on the left Y axis on the logarithmic scale according to the position of the SNPs on chromosome (X axis). The horizontal solid line corresponds to a nominal pvalue of significance equal to 0.05. Horizontal dashed line corresponds the effective SNP number-corrected p-value equal to 0.0002.

for glomerular filtration rate and the presence of hypertension and diabetes, main factors for clinically relevant glomerular proteinuria (AlAly, 2013; Ferguson and Waikar, 2012). In the US general population, however, blood cadmium was associated to eGFR (Navas-Acien et al., 2009). Unfortunately, blood cadmium was not available in our study population. Importantly, for albuminuria models, we observed positive association between urinary cadmium and albuminuria both in participants with and without reduced glomerular filtration rate, hypertension or diabetes. In experimental models cadmium decreased albumin reabsorption in the proximal tubules via downregulation of megalin channels (Gena et al., 2010). Moreover, cadmium interfered with enzymatic activities of the calcium-calmodulin complex, inhibited Na + − K + − ATPase activity, and stimulated activity by MAP kinases (Hirano et al., 2005). Cadmium also affected the distribution of paracellular tight junction proteins and decreased transepithelial transport (Gunawardana et al., 2006). A number of epidemiologic studies using biomarkers of tubular damage also support the tubular toxicity of cadmium at relatively low exposure levels (Akesson et al., 2005; Noonan et al., 2002; Wallin et al., 2014; Wang et al., 2016). In Sweden (N = 1021), the urine cadmium concentrations were associated to tubular proteinuria at exposure levels lower than 1 μg/g creatinine (Järup et al., 2000). The Cadmibel study in Belgium (N = 1699) (Buchet et al., 1990) reported a positive doseresponse relationship between cadmium and urinary excretion of retinol-binding protein, N-acetyl-β-glucosaminidase, β2-microglobulin, aminoacids and calcium. Large population-based prospective studies, however, are lacking (Byber et al., 2016). Results from our gene-environment interaction analysis provide further biological insight into the potential toxic role of cadmium in the kidney at low exposure levels. For instance, we observed Bonferronicorrected statistical interaction of cadmium with rs3087816 in SLC30A7, which encodes an endosomal zinc transporter, both in continuous albuminuria and abnormal albuminuria models. Cadmium competes with other metals for transporter-mediated cell entry, among which the Zinc-regulated transporters, the so-called ZIP proteins, play a major role. Knockdown of ZIP-8 and ZIP-14, located in proximal tubular

cells of the kidney, resulted in significantly reduced tubular cadmium uptake in mouse models (Fujishiro et al., 2012). In humans, SCL30A4 is not expressed in the kidney itself, however, cross-sectional studies have shown an inverse relationship between zinc concentrations and cadmium-induced nephrotoxicity, maybe reflecting decreased cadmium uptake and toxicity when zinc levels are high (Lin et al., 2014; Vance and Chun, 2015). In our gene-environment interaction, carriers of genotypes CC in rs3087816 had increased associations of cadmium and albuminuria compared to carriers of CT + TT. Interestingly, in fully adjusted models, carriers of CC genotype showed significantly increased levels of albuminuria but not cadmium (Supplemental Table 3). Altogether, our findings are compatible with a synergistic interaction of cadmium and rs3087816. Other cadmium transporter proteins located in the renal convolute tubules including metallothioneins (MTs) and divalent metal-ion transporter-1 (DMT-1), play a key role in the development of cadmium-induced nephrotoxicity (Yang and Shu, 2015). In our study, the interaction of cadmium with the available polymorphisms in genes encoding MT isoforms or SLC11A2, the gene encoding the DMT-1 transporter, which is an iron transporter, was not significant. However, in abnormal albuminuria models, we observed suggestive interactions of cadmium with de gene encoding transferrin (TF), which is related to body iron stores and may be related to the affinity of cadmium with the DMT-1 transporter in the digestive track (Gallagher et al., 2011; Lee and Kim, 2012b). Interestingly, in continuous albuminuria models, the interaction of cadmium and rs4720672, a sequence variant located within a half kilobase of the end of RAC1 gene, was statistically significant at the Bonferroni-corrected level. Proteins encoded by RAC1, a Rho-family small GTP-ase, interfere in control of cell growth, cytoskeletal reorganization and activation of protein kinases (Fritz and Henninger, 2015). In vitro models in rat adrenal medulla cell lines (PC12) and human bone marrow cell lines (SH-SY5Y) have shown cadmium-induced generation of reactive oxygen species (ROS) by upregulating the expression of NADPH oxidase 2 and its regulatory proteins (RAC1, among others) (Chen et al., 2011). Upregulation of RAC1 has been associated with albuminuria and proximal tubulopathy (Babelova et al.,

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tion at a nominal p-value of 0.05, were located in genes encoding proteins previously associated to cadmium in mechanistic studies. For instance, cadmium has been demonstrated to disturb the transcriptional status of CAT, the gene encoding the catalase enzyme, in mice kidneys and other tissues, interfering in adaption and survival of cadmiumexposed cells (Jin et al., 2015; Thijssen et al., 2007). Furthermore, in both in vitro and in vivo studies (Al Bakheet et al., 2013; Bravard et al., 2006; Pizzino et al., 2014; Youn et al., 2005), cadmium has been associated with impaired expression of OGG1, whose activation results in excision of 8-oxoguanine, a mutagenic base product (Zhou et al., 2015). In kidney mice models, exposure to cadmium leads to nuclear translocation and accumulation of redox-active thioredoxin-1 (Trx1), a cytoplasmic protein that translocates to nuclei during oxidative stress and is involved in many redox reactions, resulting in inflammation and cell death (Go et al., 2013). In addition, GSR encodes the glutathione reductase, which reduces oxidized glutathione disulfide (GSSG) to the sulfhydryl form GSH, and was inhibited by cadmium in experimental settings (Acan and Tezcan, 1995; Serafini et al., 1989; Ulusu et al., 2003). More powerful gene-environment interaction studies are needed to confirm the relevance of these statistical interactions in human populations. The present study has to be considered with its limitations and strengths. First, the cross-sectional design does not allow establishing the temporality of the observed associations. While the identified geneenvironment interactions point to a biological connection between cadmium, oxidative stress and albuminuria levels, we cannot discard whether processes that influence transport of cadmium and albumin, including in the tubules, may be potentially related to a co-excretion of cadmium and albumin in the urine. Similarly, the present data do not allow to elucidate if the association with albuminuria is of glomerular or tubular origin or both. Unfortunately, the possibility of reverse causality is hard to discard, as well as the opposite. Second, while urine cadmium is an established biomarker of cumulative exposure and internal dose, scientific debate is emerging about its potential limitations given the large within-individual variability in urinary cadmium excretion (Akerstrom et al., 2013; Gunier et al., 2013). For instance, while traditionally it is believed that urinary cadmium has a very long half-life, recent studies have shown that recent exposure to cadmium can also influence urinary cadmium excretion, as short-term changes in exposure to tobacco smoke follow changes in urinary cadmium in both active and passive smokers (Adams and Newcomb, 2014; SánchezRodríguez et al., 2015). In addition, in our study, as in most epidemiologic studies, we used spot urine samples, which required adjustment for urine dilution by urine creatinine. Creatinine, a breakdown product of creatine phosphate in muscle, is generally produced at a constant rate depending on muscle mass (Heymsfield et al., 1983). Overall, we cannot discard the possibility that our results are influenced by non-differential measurement error, short-term changes in exposure to cadmium and physiological urinary albumin and creatinine excretion. Third, the genes included in the analysis were pre-selected based on a priori hypothesis. Therefore, potentially relevant SNPs also interacting with cadmium and kidney function on genes could have been missed. Strengths of the present study include the sampling design that allows the findings to be generalized to a general population from Spain, the availability of detailed information for adjusting for the main risk factors and the availability of genotyped SNPs for 107 candidate genes associated with oxidative stress and albuminuria pathways.

Table 5 Geometric mean ratio (95% confidence interval) of albuminuria levels comparing 80th vs. 20th percentiles of cadmium distribution by combination of rs3087816 in SLC30A7 and rs4720672 in RAC1 genotypes (3-way interaction). SLC30A7 rs3087816 (REC)

RAC1 rs4720672 (DOM)

N

GMR (95% CI)

P int.

T/T + T/C (ref) T/T + T/C (ref) C/C C/C

T/T (ref) T/C + C/C T/T (ref) T/C + C/C

892 344 34 14

1.82 3.02 2.43 19.1

< 0.001

(1.65, (1.85, (2.07, (8.02,

2.01) 4.94) 2.85) 45.49)

Abbreviations: GMR, geometric mean ratio; CI, confidence interval; REC, recessive model; DOM, dominant model. Model was adjusted for age (splines), gender (male, female), educational level (< high school, > = high school), body mass index (kg/m2), smoking status (never, former, current), cumulative smoking dose (pack-year), urine cotinine (< 34, 34–500, > 500 mg/dL), alcohol intake (mg/day), hypertension status (no, yes), type 2 2 diabetes (no, yes) and glomerular filtration rate (mL/min/1.73 m ).

2013; Whaley-Connell et al., 2007). Animal models have shown that RAC1 also plays a key role in the maintenance of podocytes integrity (Blattner et al., 2013). In our study population, rs4720672 in RAC1 was significantly associated to albuminuria. The association of this polymorphism with cadmium, however, was only marginally significant (Supplemental Table 3). While we cannot conclude that RAC1 upregulation is causally involved in cadmium-induced nephrotoxicity, the strong interaction between urinary cadmium and genetic variation in RAC1 on albuminuria levels supports the possibility that the association of cadmium with albuminuria reflects a biological link. In post-hoc analysis, we evaluated the 3-way interaction of rs3087816 in SLC30A7, rs4720672 in RAC1 and cadmium (Table 5). The geometric mean ratio comparing the 80th to the 20th percentile of cadmium distributions among carriers of both wildtype genotypes were 10 times higher compared to the corresponding association among carriers of reference genotypes in both genes. Alternatively, RAC1-mediated activation of NADPH oxidase follows overproduction of ROS in the vascular wall and is involved in smooth muscular proliferation, cardiomyocyte hypertrophy, endothelial cell shape change, atherosclerosis and endothelial dysfunction (Carrizzo et al., 2014). While cadmium has also been associated to endothelial dysfunction in experimental settings (Almenara et al., 2013; Kukongviriyapan et al., 2014, 2016; Lukkhananan et al., 2015; Messner et al., 2009), the implications for microvascular renal disease are, however, unknown. It is also possible that the associations of cadmium with albuminuria reflect endothelial damage not only in the kidney but also in other vascular territories, as albuminuria is considered a biomarker of endothelial dysfunction (Bartz et al., 2015; Pedrinelli et al., 2001; Stehouwer and Smulders, 2006). In addition, in abnormal albuminuria recessive models, we found a strong statistical interaction of cadmium and rs9610684 in RAC2, another member of the RAC subfamily of Rho G proteins, a finding that needs to be interpreted cautiously given the low number of abnormal albuminuria cases in the minor genotype (Supplemental Table 2). Increased oxidative stress is a generally accepted mechanism for cadmium toxicity (Liu et al., 2008). In in vitro studies, free cadmium in the tubular cell accumulated in mitochondria and blocked the respiratory chain at complex III, resulting in mitochondrial dysfunction and the generation of ROS (Wang et al., 2010, 2004). Experimental data supports that cadmium may also be a direct inhibitor of mitochondrial respiratory complex I (Belyaeva et al., 2004). Interestingly, we observed marginally significant statistical interactions of cadmium with rs3848638 in NDUFS7, which encodes the NADH:ubiquinone oxidoreductase (a subunit of complex I in the mitochondrial respiratory chain), on abnormal albuminuria. Free cadmium also binds to protein sulfhydryl groups and affects the structure and function of antioxidant proteins (Wu et al., 2016). Finally, some of the candidate SNPs that showed statistical interac-

4.1. Conclusions Cadmium exposure showed strong positive associations with albuminuria at relatively low levels of exposure present in a general population from Spain. Findings from candidate gene-cadmium interaction analyses are consistent with available evidence from mechanistic studies on cadmium toxicity, and support that certain genotypes may 33

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confer differential susceptibility to potential cadmium effects. Prospective epidemiologic studies are needed to confirm the relevance of potential gene-cadmium interactions in relation to clinical albuminuria in human populations with low levels cadmium exposure. Support and financial disclosure declaration This work was supported by the Strategic Action for Research in Health sciences [CP12/03080, PI10/0082, PI13/01848, PI07/0497, PI14/00874, PI15/00071 and PI11/00726], GRUPOS 03/101; PROMETEO/2009/029 and 2005/027, AMP07/075 and ACOMP/ 2013/039 from the Valencia Government, GRS/279/A/08 from Castilla-Leon Government and European Network of Excellence Ingenious Hypercare (EPSS- 037093) from the European Commission; CIBER Fisiopatología Obesidad y Nutrición (CIBERobn) [CIBER-02-082009, CB06/03 and CB12/03/30016] and CIBER de Diabetes y Enfermedades Metabólicas Relacionadas (CIBERDEM) [CB07/0/018]. The Strategic Action for Research in Health sciences, Retics, CIBEROB and CIBERDEM are initiatives from Carlos III Health Institute Madrid and the Spanish Ministry of Economy and Competitiveness and cofunded with European Funds for Regional Development (FEDER). Conflicts of interest None declared. Acknowledgements None. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.envint.2017.05.008. References Acan, N.L., Tezcan, E.F., 1995. Inhibition kinetics of sheep brain glutathione reductase by cadmium ion. Biochem. Mol. Med. 54, 33–37. Adams, S.V., Newcomb, P.A., 2014. Cadmium blood and urine concentrations as measures of exposure: NHANES 1999-2010. J. Expo. Sci. Environ. Epidemiol. 24, 163–170. http://dx.doi.org/10.1038/jes.2013.55. Akerstrom, M., Sallsten, G., Lundh, T., Barregard, L., 2013. Associations between urinary excretion of cadmium and proteins in a nonsmoking population: renal toxicity or normal physiology? Environ. Health Perspect. 121, 187–191. http://dx.doi.org/10. 1289/ehp.1205418. Akesson, A., Lundh, T., Vahter, M., Bjellerup, P., Lidfeldt, J., Nerbrand, C., Samsioe, G., Strömberg, U., Skerfving, S., 2005. Tubular and glomerular kidney effects in Swedish women with low environmental cadmium exposure. Environ. Health Perspect. 113, 1627–1631. Al Bakheet, S.A., Attafi, I.M., Maayah, Z.H., Abd-Allah, A.R., Asiri, Y.A., Korashy, H.M., 2013. Effect of long-term human exposure to environmental heavy metals on the expression of detoxification and DNA repair genes. Environ. Pollut. 181, 226–232. (Barking Essex 1987). http://dx.doi.org/10.1016/j.envpol.2013.06.014. Al-Aly, Z., 2013. Prediction of renal end points in chronic kidney disease. Kidney Int. 83, 189–191. http://dx.doi.org/10.1038/ki.2012.418. Almenara, C.C.P., Broseghini-Filho, G.B., Vescovi, M.V.A., Angeli, J.K., Faria, T. de O., Stefanon, I., Vassallo, D.V., Padilha, A.S., 2013. Chronic cadmium treatment promotes oxidative stress and endothelial damage in isolated rat aorta. PLoS One 8, e68418. http://dx.doi.org/10.1371/journal.pone.0068418. Babelova, A., Jansen, F., Sander, K., Löhn, M., Schäfer, L., Fork, C., Ruetten, H., Plettenburg, O., Stark, H., Daniel, C., Amann, K., Pavenstädt, H., Jung, O., Brandes, R.P., 2013. Activation of Rac-1 and RhoA contributes to podocyte injury in chronic kidney disease. PLoS One 8, e80328. http://dx.doi.org/10.1371/journal.pone. 0080328. Barregard, L., Bergström, G., Fagerberg, B., 2014. Cadmium, type 2 diabetes, and kidney damage in a cohort of middle-aged women. Environ. Res. 135, 311–316. http://dx. doi.org/10.1016/j.envres.2014.09.017. Bartz, S.K., Caldas, M.C., Tomsa, A., Krishnamurthy, R., Bacha, F., 2015. Urine albuminto-creatinine ratio: a marker of early endothelial dysfunction in youth. J. Clin. Endocrinol. Metab. 100, 3393–3399. http://dx.doi.org/10.1210/JC.2015-2230. Belyaeva, E.A., Glazunov, V.V., Korotkov, S.M., 2004. Cd2+ versus Ca2 +− produced mitochondrial membrane permeabilization: a proposed direct participation of respiratory complexes I and III. Chem. Biol. Interact. 150, 253–270. http://dx.doi.

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