Supplementary Material
for
Benchmark Dose for Cadmium Exposure and Elevated N-acetyl-β-D-glucosaminidase: A Meta-analysis
CuiXia Liua,b, YuBiao Lic, ChunShui Zhub,d, ZhaoMin Dong*,b,e, Kun Zhangf, YanBin Zhaof, YiLu Xue
a School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
b Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia
c School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, Hubei, China
d School of Chemical Engineering, Huaihai Institute of Technology, LianYungang, JiangSu 222005, China
e Global Centre for Environmental Remediation, the Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
f University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Gründenstrasse 40, CH-4132 Muttenz, Switzerland
* Corresponding Author: Zhaomin Dong
ATC Building, Global Center for Environmental Remediation, Faculty of Science and Information Technology, University of Newcastle, Callaghan, NSW 2308
Email:
Literature data:
The collected literature data is provided in .csv format (rawdata.csv).
Text:
Data Transformation:
Since various data types were used in previous analyses, most studies’ data can be transformed into geometric mean (GM) and geometric standard derivation (GSD) under the following scenarios:
1, Given a lognormal distribution of a parameter for which and are the mean and the standard deviation of the distribution, respectively. GM, arithmetic mean (AM), GSD, and standard derivation (SD) represent the geometric mean, arithmetic mean, geometric standard deviation, standard deviation of the distribution, respectively. The following relationships hold:
S1
S2
S3
S4
Thus, the GM and GSD can be calculated as:
S5
S6
2, In cases where the median and range were reported, the GM and GSD were calculated as:
S7
S8
where stands for the cumulative density function of the normalized Gaussian distribution, and n is the sample size;
3, If the (1-α) confidence interval (CI) was reported (it is common for 95% CI to be used when α is set to 5%), the GSD was retrieved as:
S9
Where and are the 1-α/2 and α/2 percentiles, respectively, and the t denotes the student-t distribution with df of n-1 (sample size minus 1).
4, The relationship between standard derivation error (SE) and SD holds:
S10
where n is the sample size.
WinBUGS model code:
model{
for (i in 1: nrow){
y[i]~dnorm(myt[i],tauy[i])
myt[i]~dnorm(my[i],tau)
mx[i]~dnorm(x[i],taux[i])
my[i]<- alpha1[study[i]]+beta*mx[i]}
for (i in 1:nstudy) {alpha1[i]~dnorm(alpha,pstudy)}
sigmastudy ~ dunif(0,10)
sigmatau~dunif(0,10)
pstudy<- 1 / pow(sigmastudy,2)
tau <- 1 / pow(sigmatau,2)
alpha~dunif(-100,100)
beta~dunif(-100,100)
}
Figure S1. Scatter plot between predicted and observed N-acetyl-β-D-glucosaminidase (NAG) for the log-log linear model fitted to the complete data set. Log-Log scale was used for X-axis and Y-axis.
Figure S2. Trend between literature publication year and estimated 95% lower confidence limit of benchmark doses (BMDL) for log-log linear model. The BMDL was estimated when including literature published before the target year. BMDL5, BMDL at benchmark dose (BMR) is 5%; BMDL10 at BMR is 10%.
Figure S3. Relationships between the cut-off and benchmark doses (BMDs) for log-log linear model. Log-Log scale was used for X-axis and Y-axis. BMDL5, 95% lower confidence limit of BMD (BMDL) at benchmark dose (BMR) is 5%; BMDL10 at BMR is 10%; cr, creatinine.
Table S1. Study-ID and Corresponding References
1 / Jung, K.; Pergande, M.; Graubaum, H. J.; Fels, L. M.; Endl, U.; Stolte, H., Urinary proteins and enzymes as early indicators of renal dysfunction in chronic exposure to cadmium. Clinical Chemistry 1993, 39, (5), 757-765.
2 / Kido, T.; Kobayashi, E.; Hayano, M.; Nogawa, K.; Tsuritani, I.; Nishijo, M.; Tabata, M.; Nakagawa, H.; Nuyts, G. D.; Broe, M. E. D., Significance of elevated urinary human intestinal alkaline phosphatase in Japanese people exposed to environmental cadmium. Toxicology Letters 1995, 80, (1-3), 49-54.
3 / Järup, L., .; M D, C.; Elinder, C. G.; Hellström, L., .; Persson, B., .; Schütz, A., . Enzymuria in a population living near a cadmium battery plant. Occupational & Environmental Medicine 1995, 52, (11), 770-772.
4 / Reeves, P. G.; Vanderpool, R. A., Cadmium burden of men and women who report regular consumption of confectionery sunflower kernels containing a natural abundance of cadmium. Environmental Health Perspectives 1997, 105, (10), 1098-104.
5 / Suwazono, Y., .; Kobayashi, E., .; Okubo, Y., .; Nogawa, K., .; Kido, T., .; Nakagawa, H., . Renal effects of cadmium exposure in cadmium nonpolluted areas in Japan. Environmental Research 2000, 84, (1), 44–55.
6 / Jin, T.; Nordberg, M.; Frech, W.; Dumont, X.; Bernard, A.; Ye, T. T.; Kong, Q.; Wang, Z.; Li, P.; Lundström, N. G., Cadmium biomonitoring and renal dysfunction among a population environmentally exposed to cadmium from smelting in China (ChinaCad). Biometals An International Journal on the Role of Metal Ions in Biology Biochemistry & Medicine 2002, 15, (4), 397-410.
7 / Noonan, C. W.; Sarasua, S. M.; Dave, C.; Kathman, S. J.; Lybarger, J. A.; Mueller, P. W., Effects of Exposure to Low Levels of Environmental Cadmium on Renal Biomarkers. (Articles). Environmental Health Perspectives 2002, 110, (2), 151-155.
8 / Ing-Marie, O.; Inger, B.; Thomas, L.; Helena, O.; Staffan, S.; Agneta, O., Cadmium in Blood and Urine—Impact of Sex, Age, Dietary Intake, Iron Status, and Former Smoking—Association of Renal Effects. Environmental Health Perspectives 2002, 110, (12), págs. 1185-1190.
9 / Nakadaira, H.; Nishi, S., Effects of low-dose cadmium exposure on biological examinations. Science of the Total Environment 2003, 308, (1-3), 49–62.
10 / Honda, R.; Tsuritani, I.; Noborisaka, Y.; Suzuki, H.; Ishizaki, M.; Yamada, Y., Urinary cadmium excretion is correlated with calcaneal bone mass in Japanese women living in an urban area. Environmental Research 2003, 91, (2), 63-70.
11 / Jin, T.; Kong, Q.; Ye, T.; Wu, X.; Nordberg, G. F., Renal dysfunction of cadmium-exposed workers residing in a cadmium-polluted environment. Biometals 2004, 17, (5), 513-518(6).
12 / Satarug, S.; Ujjin, P.; Vanavanitkun, Y.; Nishijo, M.; Baker, J. R.; Moore, M. R., Effects of cigarette smoking and exposure to cadmium and lead on phenotypic variability of hepatic CYP2A6 and renal function biomarkers in men. Toxicology 2004, 204, (2-3), 161-173.
13 / Tsukasa, U.; Etsuko, K.; Yasushi, S.; Yasushi, O.; Katsuyuki, M.; Kiyomi, S.; Akira, O.; Hirotsugu, U.; Hideaki, N.; Koji, N., Health effects of cadmium exposure in the general environment in Japan with special reference to the lower limit of the benchmark dose as the threshold level of urinary cadmium. Scandinavian Journal of Work Environment & Health 2005, 31, (4), 307-315.
14 / Hong, F.; Jin, T.; Zhang, A., Risk assessment on renal dysfunction caused by co-exposure to arsenic and cadmium using benchmark dose calculation in a Chinese population. Biometals An International Journal on the Role of Metal Ions in Biology Biochemistry & Medicine 2004, 17, (5), 573-580.
15 / Nordberg, G. F.; Jin, T., .; Hong, F., .; Zhang, A., .; Buchet, J. P.; Bernard, A., . Biomarkers of cadmium and arsenic interactions. Toxicology & Applied Pharmacology 2005, 206, (2), 191–197.
16 / Moriguchi, J.; Ezaki, T.; Tsukahara, T.; Fukui, Y.; Ukai, H.; Okamoto, S.; Shimbo, S.; Sakurai, H.; Ikeda, M., Effects of aging on cadmium and tubular dysfunction markers in urine from adult women in non-polluted areas. International Archives of Occupational & Environmental Health 2005, 78, (6), 446-451.
17 / Suwazono Y., Sand S., Vahter M., Filipsson AF., Skerfving S., Lidfeldt J., Åkesson A.; Benchmark dose for cadmium-induced renal effects in humans. Environmental Health Perspectives 2006, 114, (7), 1072-1076.
18 / Teeyakasem, W.; Nishijo, M.; Honda, R.; Satarug, S.; Swaddiwudhipong, W.; Ruangyuttikarn, W., Monitoring of cadmium toxicity in a Thai population with high-level environmental exposure. Toxicology Letters 2007, 169, (3), 185–195.
19 / Tosukhowong, P.; Boonla, C.; Prapunwattana, P.; Supapong, S.; Tungsanga, K.; Mahasakpun, P., Renal impairment and Stone risk in inhabitants environmentally exposed to cadmium in Mae Sot District of Tak Province, Thailand. Asian Biomedicine 2008, 2, (1), 59-66.
20 / Yamagami, T.; Suna, T.; Fukui, Y.; Ohashi, F.; Takada, S.; Sakurai, H.; Aoshima, K.; Ikeda, M., Biological variations in cadmium, alpha 1-microglobulin, beta 2-microglobulin and N-acetyl-beta-D-glucosaminidase in adult women in a non-polluted area. International Archives of Occupational & Environmental Health 2008, 81, (3), 263-271.
21 / Huang, M.; Choi, S. J.; Kim, D. W.; Kim, N. Y.; Park, C. H.; Yu, S. D.; Kim, D. S.; Park, K. S.; Song, J. S.; Kim, H., Risk assessment of low-level cadmium and arsenic on the kidney. Journal of Toxicology & Environmental Health Part A 2009, 72, (21-22), 1493-1498.
22 / Honda, R.; Swaddiwudhipong, W.; Nishijo, M.; Mahasakpan, P.; Teeyakasem, W.; Ruangyuttikarn, W.; Satarug, S.; Padungtod, C.; Nakagawa, H., Cadmium induced renal dysfunction among residents of rice farming area downstream from a zinc-mineralized belt in Thailand. Toxicology Letters 2010, 198, (1), 26-32.
23 / Ikeda, M.; Fukui, Y.; Ohashi, F.; Sakuragi, S.; Moriguchi, J., Low Cadmium Levels in Urine of Residents in two Prefectures where Cadmium Levels in Locally Harvested Brown Rice are Higher than in other Prefectures in Japan. Biological Trace Element Research 2011, 139, (2), 217-227.
24 / Ikeda, M.; Ohashi, F.; Fukui, Y.; Sakuragi, S.; Moriguchi, J., Closer correlation of cadmium in urine than that of cadmium in blood with tubular dysfunction markers in urine among general women populations in Japan. International Archives of Occupational & Environmental Health 2011, 84, (2), 121-129.
25 / Suwazono, Y.; Nogawa, K.; Uetani, M.; Kido, T.; Nakagawa, H., Reassessment of the threshold of urinary cadmium by using hybrid approach in a cadmium non-polluted area in Japan. International Journal of Hygiene & Environmental Health 2011, 214, (2), 175–178.
26 / Boonprasert, K.; Ruengweerayut, R.; Satarug, S.; Na-Bangchang, K., Study on the association between environmental cadmium exposure, cytochrome P450-mediated 20-HETE, heme-oxygenase-1 polymorphism and hypertension in Thai population residing in a malaria endemic areas with cadmium pollution. Environmental Toxicology & Pharmacology 2011, 31, (3), 416–426.
27 / Zhang, C.; Liang, Y.; Lei, L.; Zhu, G.; Chen, X.; Jin, T.; Wu, Q., Hypermethylations of RASAL1 and KLOTHO is associated with renal dysfunction in a Chinese population environmentally exposed to cadmium. Toxicology & Applied Pharmacology 2013, 271, (1), 78–85.
28 / Ruangyuttikarn, W.; Panyamoon, A.; Nambunmee, K.; Honda, R.; Swaddiwudhipong, W.; Nishijo, M., Use of the kidney injury molecule-1 as a biomarker for early detection of renal tubular dysfunction in a population chronically exposed to cadmium in the environment. Springerplus 2013, 2, (1), 533-533.
29 / Nishijo, M.; Suwazono, Y.; Ruangyuttikarn, W.; Nambunmee, K.; Swaddiwudhipong, W.; Nogawa, K.; Nakagawa, H., Risk assessment for Thai population: benchmark dose of urinary and blood cadmium levels for renal effects by hybrid approach of inhabitants living in polluted and non-polluted areas in Thailand. Bmc Public Health 2014, 14, (15), 1-9.
30 / Kim, Y. D.; Yim, D. H.; Eom, S. Y.; Moon, S. I.; Park, C. H.; Kim, G. B.; Yu, S. D.; Choi, B. S.; Park, J. D.; Kim, H., Temporal changes in urinary levels of cadmium, N-acetyl-beta-D-glucosaminidase and beta(2)-microglobulin in individuals in a cadmium-contaminated area. Environmental Toxicology & Pharmacology 2014, 39, (1), 35–41.
Table S2. Fitted parameters for Meta-regression for Asians.
Japanese
(n=32) / Mean (std) / 0.53 (0.09) / 0.87 (0.25) / 0.20 (0.04) / -76.89
Median (95%CI) / 0.53 (0.35, 0.71) / 0.87 (0.39, 1.36) / 0.19 (0.13, 0.30)
Chinese
(n=16) / Mean (std) / 0.71 (0.05) / 0.71 (0.37) / 0.09 (0.057) / -18.63
Median (95%CI) / 0.71 (0.60, 0.80) / 0.71 (0.018, 1.43) / 0.081 (0.002, 0.22)
Thais
(n=18) / Mean (std) / 0.31 (0.098) / 1.03 (0.30) / 0.22 (0.048) / -32.32
Median (95%CI) / 0.30 (0.11, 0.50) / 1.03 (0.44, 1.59) / 0.22 (0.15, 0.34)
The others
(n=14) / Mean (std) / 2.41 (2.75) / -1.51 (4.04) / 0.90 (0.64) / -14.66
Median (95%CI) / 1.64 (-2.05, 8.41) / -0.48 (-11.46, 5.33) / 0.77 (0.059, 2.48)
Table S3. BMD/BMDL estimation (µg/g. creatinine) when removing each study.
1 / 73 / 1.77 / 1.68 / 2.02 / 1.86
2 / 80 / 1.70 / 1.61 / 1.95 / 1.78
3 / 72 / 1.76 / 1.67 / 2.01 / 1.85
4 / 40 / 1.84 / 1.74 / 2.11 / 1.93
5 / 2759 / 1.60 / 1.52 / 1.85 / 1.69
6 / 790 / 1.71 / 1.62 / 1.98 / 1.81
7 / 310 / 1.89 / 1.80 / 2.16 / 1.98
8 / 95 / 1.89 / 1.80 / 2.17 / 1.99
9 / 148 / 1.76 / 1.67 / 2.01 / 1.85
10 / 908 / 1.75 / 1.67 / 1.99 / 1.84
11 / 176 / 1.69 / 1.61 / 1.94 / 1.78
12 / 43 / 1.82 / 1.74 / 2.08 / 1.92
13 / 828 / 1.78 / 1.68 / 2.05 / 1.87
14 / 123 / 1.76 / 1.68 / 2.01 / 1.86
15 / 374 / 1.70 / 1.61 / 1.96 / 1.79
16 / 11090 / 1.77 / 1.68 / 2.02 / 1.86
17 / 790 / 1.77 / 1.68 / 2.02 / 1.86
18 / 128 / 1.75 / 1.67 / 2.00 / 1.85
19 / 77 / 1.76 / 1.68 / 2.00 / 1.85
20 / 17 / 1.76 / 1.67 / 2.01 / 1.85
21 / 290 / 1.83 / 1.74 / 2.10 / 1.93
22 / 795 / 1.78 / 1.69 / 2.06 / 1.88
23 / 1404 / 1.76 / 1.68 / 2.01 / 1.86
24 / 1403 / 1.76 / 1.68 / 2.02 / 1.86
25 / 1270 / 1.83 / 1.74 / 2.09 / 1.93
26 / 154 / 1.77 / 1.68 / 2.03 / 1.86
27 / 81 / 1.76 / 1.68 / 2.02 / 1.86
28 / 700 / 1.76 / 1.68 / 2.02 / 1.86
29 / 616 / 1.75 / 1.67 / 1.98 / 1.83
30 / 417 / 1.50 / 1.45 / 1.63 / 1.55
Combined All Studies / 38250 / 1.76 / 1.67 / 2.01 / 1.86
Table S4. Estimated BMD/BMDL (µg/g. cr) based on log-logistic, Probit and logistic models (n=92).