ERF of dioxin
Moderator:Jouni (see all) 

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ERF of dioxin describes quantitative relationships between exposure to polychlorinated dibenzopdioxins, polychlorinated dibenzofurans (PCDD/Fs), and polychlorinated biphenyls (PCBs) and several health effects such as cancer, developmental defects and others. It assumed that effects are mediated via the Ah receptor and that toxic equivalencies (TEFs)) apply.
Question
What are quantitative relationships between exposure to dioxin (a common name for polychlorinated dibenzopdioxins, polychlorinated dibenzofurans (PCDD/Fs), and polychlorinated biphenyls (PCBs)) and several health impacts including
 remaining lifetime cancer risk and
 developmental defects in molar teethR↻
Answer
<rcode embed=1> library(OpasnetUtils)
objects.latest("Op_en5823", code_name = "initiate") # ERF of dioxin ovariables ERF, threshold.
oprint(summary(EvalOutput(ERF_diox)))
</rcode>
Rationale
Dioxins are a group of polychlorinated dibenzopdioxins (PCDDs) and dibenzofurans (PCDFs). They are persistent environmental contaminants that accumulate in the human body. Their elimination halflife is quite high (~7 years). 2,3,7,8tetrachlorodibenzopdioxin (TCDD) is the most toxic PCDD/Fs congener, and it is classified as a known human carcinogen by the International Agency for Research on Cancer (IARC).
 Health effects related to longterm exposure
 impairment of the immune system
 impairment of the developing nervous system
 impairment of the endocrine system
 impairment of reproductive functions
 increased cancer risk
For human health impact assessment recommended toxicity equivalence factors (TEFs) are used to convert toxicity of PCDD/F or PCB congeners in relations to TCDD.
There is evidence that exposure to TCDD as boys decreases sperm count and motility in men EPA summary of dioxin with a LOAEL of (0.020 ng/kg day).
Data
 Human health effects caused by dioxins
Dioxins are persistent environmental pollutants and they accumulate in the food chain. Dioxins cause a large variety of effects in laboratory animals. They are carcinogenic at large doses, and they also cause developmental defects. The evidence of human effects has been more limited, because the exposure levels have been much lower than in animal tests. However, an increased cancer risk has been observed after high industrial occupational exposures. In addition, mild tooth mineralisation defects have been observed in children in Finland, even after typical exposures of the 1980's. Children are exposed to dioxins mostly via mother's milk. The dioxin levels have been decreasing since then, and no tooth defects have been observed at the current exposure levels.
<t2b index="Exposure agent,Response,Exposure,Exposure unit,ER function,Scaling,Observation" locations="Threshold,ERF" desc="Description" unit=""> TEQYes or no developmental dental defects incl. agenesisIngestion etc. (as it was in Seveso) as log(TCDD serum concentration+1) in fatlog(pg /g)ERSLog1000.26 + 0.12Alaluusua et al. 2004; PL Gradowska PhD thesis 2013. From ERF of TCDD. Resulting distribution based on one simulation. Weibull(alfa=0.2925,beta=2.192) TEQYes or no tooth defectIntake through placenta and mother's milk as log(TCDD serum concentration+1) in fatlog(pg /g)ERSLog1000:0.06:0.12Alaluusua et al. 2004 data with PL Gradowska PhD thesis 2013 approach but we used the response function y = k x + b (see below) TEQYes or no dental defectIntake through placenta and mother's milkpg /gERSNone00:0.001382:0.0028Alaluusua et al. 1996. Mother's exposure must be converted to child's exposure (measured as pg /g fat) using ovariable Infant's dioxin exposure. Uncertainty +100 % based on author judgement TEQCancer morbidityIngested intakepg /kg /dCSFBW00  0.001U.S.EPA 2004: 10E3 (pg/kg/d)^1. Lifetime probability TEQCancer morbidity yearlyIngested intakepg /kg /dCSFBW00  0.00002U.S.EPA 2004: 10E3 (pg/kg/d)^1. Lifetime probability divided by 50 to produce average yearly risk TEQDioxin recommendation tolerable daily intakeIngested intakepg /kg /dTDIBW02TWI 14 pg/kg/week by SCF (2001). TEQDioxin recommendation tolerable daily intake 2018Ingested intakepg /kg /dTDIBW00.2889TWI 2 pg/kg/week by EFSA committee (2018). TEQSperm concentrationIntake through placenta and mother's milkpg /gERSNone00.00006+0.00004MinguezAlarcon 2017. Mother's exposure must be converted to child's exposure (measured as pg /g fat) using ovariable Infant's dioxin exposure. See details below. </t2b>
ERF of dioxin on cancer is indexed by age. It applies to adult age categories, > 18 years (gender combined).
SCF (2001). Scientific Committee on Food of the European Commission.^{[1]}
Sperm concentration
In humans, sperm concentrations have been shown to decrease permanently if boys are exposed to dioxins before nine years of age. The results come from Seveso^{[2]}^{[3]} and a Russian children's study^{[4]}.
EFSA recently assessed this risk and derived doseresponse from the Russian children's study. In the study, the boys were divided into quartiles with PCDD/F TEQ values 7.0, 10.9, 15.2, and 32.8 pg/g fat. Sperm concentration mean was 65 (95 % CI 5080) million/ml in the lowest quartile, while in all other quartiles the concentration was 40 (96 % CI 3055) million/ml. (Numbers are approximate because they have been read from a figure.)
According to a review^{[5]}, sperm concentrations have declined from 120 to 60 million/ml between 1930 and 1990. At the same time, the fraction of men in "subfertile" range (sperm concentrations below 40 million/ml) has increased from 20 to 40 %. Above 40 million/ml, the success rate of couples who try to get pregnant is 65 % in 6 months. Below that concentration, the probability is fairly proportional to the sperm concentration.
Based on this, we estimate that there is an exposureresponse function of dioxin on sperm concentration that has the nonlinear shape of relative Hill and reduces the sperm count by 39 % (I_{max}), i.e. from 65 to 40 million/ml, and the exposure causing half of the maximal effect is 10.5 pg/g fat (the concentration of the second quartile in the Russian children's study). In five years (assuming independent probabilities between 6month periods), the probability of not getting pregnant follows this curve:
<math>P(infertility after 5 a) = (1  0.65 (1+ \frac{0.39c}{c + 10.5 pg/g})^{10},</math>
where c is the dioxin concentration in boy's fat tissue.
This curve is pretty linear below 50 pg/g with slope ca. 0.00006 g/pg, meaning that for each 1 pg/g increase in dioxin concentration the boy's fat tissue (or serum fat), there is an incrementally increased probability of 0.00006 that he cannot get a child even after five years of trying. Let's assume that five years is a critical time window, and after that the boy will be childless. Childlessness is said to be "tragedy of life", so the disability weight could be in the order of 0.1 DALY per year permanently (50 years). However, the disability weight applies to only half of the children (boys). Therefore, the impact is 0.1*50*0.5 DALY/case = 2.5 DALY/case, with rather high uncertainty (say, 05 DALY/case).
We can also consider men that have already decreased semen concentrations from an unrelated reason. Dioxin is likely to reduce that even further. For example, if the concentration is 10 million/ml, the probability of infertility in five years is 0.32, and that increases to 0.48 at dioxin concentration 100 pg/g. This exposureresponse is nonlinear, with half of the effect occurring already at 10 pg/g. If ten percent of the population had this low semen concentration and if 20 % of boys exceed 10 pg/g (as seems to be the case with Goherr assessment), then we would see for example in Finland 25000 boys/year * 0.1 with low fertility * 0.2 with high dioxin * 0.08 absolute increase in infertility = 40 cases per year and thus 50 DALY.
<rcode label="Print infertility ERF" graphics=1 embed=1>
 This is code op_en####/ on page ERF of dioxin
library(OpasnetUtils) library(ggplot2)
infer< function(d=0:100, s=40, y=5){
out < data.frame() for(i in d) { for(j in s) { for(k in y) { out < rbind( out, data.frame( Dioxin=i, Sperm=j, Years=k, Infertility=(10.65*min(40,j)/40*(1(0.39*i)/(i+10.5)))^(k*2) ) ) } } } out$Sperm < as.factor(out$Sperm) return(out)
}
dat<infer(s=(1:4)*10,y=c(1,3,5)) ggplot(dat,aes(x=Dioxin,y=Infertility,colour=Sperm,group=Sperm))+geom_line()+
facet_wrap(~Years)+theme_gray(base_size=24)+ labs(title="Probability of infertility after 1, 3, or 5 years of trying", x="Dioxin concentration in boys (pg/g fat)")
</rcode>
Cancer
The U.S. Environmental Protection Agency (US EPA) calculated an oral cancer slope factor (CSF) for 2,3,7,8  TCDD (the most toxic dioxin compound)^{[6]}^{[7]}. This is a citation from their summary:
 "While major uncertainties remain, efforts of this reassessment to bring more data into the evaluation of cancer potency have resulted in an estimate of 1 x 10^{3} per pgTEQ/kgBW/day. This slope factor and resulting risk specific dose estimate represents a plausible upper bound on risk based on evaluation of human and animal data within the range of observation and at a minimally detectable response level (ED01). These values are approximately 10 times higher than previous estimates (1985, 1994) which were based on fewer data. Considering the slope factors and current intake levels, upper bound (>95%ile) risks for the general population may exceed 10^{3} (1 in 1,000). "True" risks are not likely to exceed this value, are likely to be less, and may even be zero for some members of the population."
Based on this, a uniform distribution between 0 and the estimate seems plausible.
Evidence concerning cancer risk is mainly from animal studies, and dioxins are probably quite weak carcinogens in humans. Hormesis type of doseresponse is suspected^{[8]}, but not assumed here for the cancer ERF.
A previous CSF estimate from EPA (2000) and its references were archived.
Dental defects
 Some of the content was previously in Heande.
Exposureresponse functions for tooth defects caused by TCDD (studyspecific) describes studyspecific exposureresponse functions for either enamel defects in molars or missing or smaller molars.
What is the quantitative relationship between exposure to TCDD during infancy and childhood and the risk (probability) of developmental dental defects described as defects of tooth enamel? Exposure is expressed in terms of concentration of TCDD in serum lipid.
Seveso children
Serum dioxin concentration vs. dental defects (Alaluusua et al. 2004, Seveso children study)^{[9]} ^{[10]}
 ERS = 0.26 (± 0.12)
For toxicokinetic modelling, see Infant's dioxin exposure.
ERF of dioxin on dental aberrations is a continuous random variable indexed by age. It applies to the first two age groups of the Beneris population (02 and 218 years, gender combined). It has been agreed that this ERF can be applied to WHOTEQ concentration of dioxin (PCDD/F) and dioxinlike PCB in body fat.
^{[11]} ^{[12]} ^{[13]}
Probability distribution of ERF of dioxin on dental aberrations was created based data on dioxin accident in Seveso in 1976 extracted from study by Alaluusua et al. ^{[9]} This data is summarized in a table below.
Table: Developmental defects of enamel in individuals who were children (< 5 years of age) at the time of the Seveso accident by exposure group.
Exposure group  Number of exposed individuals  Number of enamel defect cases after 25 years since Seveso accident  Serum TCDD concentration range (pq/g lipid)  Mean serum TCDD concentration (pq/g lipid)  Risk (%) 
nonABR zone  39  10  40.5  26  
Exposed group 1  10  1  31226  128.5  10 
Exposed group 2  11  5  238592  415  45 
Exposed group 3  15  9  70026000  3000  60 
It has been assumed that the TCDD exposure in children from the nonABR zone follows lognormal distribution with mean 40.5 (ng/kg in fat) and geometric standard deviation 4 while the exposure in the remaining groups is loguniformly distributed over the range of serum levels reported in the table. The loglogistic model (with constant term) was chosen to model the relation between the logtransformed and scaled serum TCDD level and the probability of developmental defects of enamel. The independent variable used was ln(serum TCDD level+1). Probability distribution of the coefficient for the independent variable was constructed using the following approach. Let ni denote number of children in exposure group i and ri be the number of observed enamel defect cases in group i, i= 1,...,4.
 Sample ni exposures from distribution of TCDD serum level in group i. Denote these exposures as xj, j=1,...,75.
 Assign ri responses randomly to ni people.
 Fit the doseresponse model to simulated data, call it model p0.
 Compute predicted probability for every person in the study, i.e. compute p0(xj).
 Resample the response of each person assuming that the probability that person j responds is p0(xj), j=1,...,75.
 Refit the doseresponse model.
 Iterate steps 56 100 times.
 Repeat steps 1  7 1500 times.
 Create the density histogram of simulated estimates of regression coefficient (only positive values are kept).
 Fit parametric probability density function to the histogram.
The units used:
 (ng/kg in fat)^{1}
 (ln(ng/kg fat))^{1}
The approach described above was used to produce two different functions. First, Gradowska 2013 used logistic regression with exposure transformation log(concentration + 1). See the trait Developmental dental defects incl. agenesis in the data table. Second, a linear function P(y) = intercept + beta * x was used; P(y) is the probability of tooth defect, x is exposure (with the same transformation) and beta is the slope coefficient. See the trait Tooth defect. In this case it was assumed that there is a nondioxinrelated background that does not affect the magnitude of dioxin effect.
Show details 

cases < c(10, 1, 5, 9) # Number of children with tooth defects in different populations children < c(39, 10, 11, 15) # Total number of children out < data.frame() for(k in 1:1500) { r < c( rbinom(children[1], 1, cases[1] / children[1]), rbinom(children[2], 1, cases[2] / children[2]), rbinom(children[3], 1, cases[3] / children[3]), rbinom(children[4], 1, cases[4] / children[4]) ) x < c( # serum dioxin concentrations distributions in different groups exp(rnorm(children[1], log(40.5)  0.5 * log(4)^2, log(4))), exp(runif(children[2], log(31), log(226))), exp(runif(children[3], log(238), log(592))), exp(runif(children[4], log(700), log(26000))) ) # for(L in 1:100) { # Does not converge nicely so this is skipped fit < lm(r ~ log(x + 1)) # p < pmax(0, fit$coefficients[1] + fit$coefficients[2] * log(x + 1)) # r < rbinom(length(r), 1, p) # } out < rbind(out, data.frame(Intercept = fit$coefficients[1], Slope = fit$coefficients[2])) } rownames(out) < 1:nrow(out) out < out[out$Slope >= 0 , ] xi < exp(runif(nrow(out), 0, log(1000))) plott < data.frame(x = xi, y = out$Slope * log(xi + 1)) ggplot(plott, aes(x = x, y = y))+geom_point()+scale_x_log10()+geom_smooth() ggplot(plott, aes(x = x, y = y))+geom_point()+geom_smooth() hist(out$Slope) # A fairly good fit for slope is Triangular(0, 0.06, 0.12) using log(TEQ pg/g in serum fat + 1) as the exposure parameter and yes/no dental defects as response) 
 A previous attempt to model dental defects is here. Note that the hidden box only shows well in the edit mode.
Finnish children
Alaluusua and coworkers studied children from Finnish general population born in 1987. ^{[14]} ^{[15]} They estimated dioxin exposure by using area under curve:
 <math>AUC = \frac{C (1  e^{k_e t})}{k_e},</math>
where AUC = area under the curve (pg a /g) C = concentration in mother's milk (pg /g) k_{e} = mother's elimination rate for dioxin during lactation (0.2877 /a) t = time of nursing (a)
Outcome  Number of children with exposure (pg*year/g milk fat)  

Low exposure (<8.0)  Moderate exposure (8.016)  High exposure (>16)  
Normal  22  41  22 
Mild defect in only one tooth  1  5  2 
Moderate defect or mild defect in more than one tooth  0  3  4 
Severe defect  0  0  2 
All  23  49  30 
We need to convert the AUC to mother's dioxin daily intake. For toxicokinetic background, see Infant's dioxin exposure.
 <math>C_{s,m} = \frac{I_{a,m} * t_{1/2,m} * f_m}{ln2 * BF_i},</math>
where C_{s,m} = dioxin concentration in serum (or fat or milk) in the mother (pg/g fat) I_{a,m} = average daily intake of dioxin of the mother in absolute amounts pg/day t_{1/2,m} = the halflife of dioxin in the mother when not nursing (2737.5 d = 7.5 a) f_{m} = fraction of ingested dioxin actually absorbing from the gut in the mother (0.80) BF_{m} = body fat amount in the mother (into which the dioxin is evenly distributed)
When C_{s,m} from this equation is put into the previous equation, we can solve I_{a,m}:
 <math>I_{a,m} = \frac{ln2 * BF * AUC * k_e}{t_{1/2,m} * f_m (1  e^{k_e t)}},</math>
where we assume an average value of 0.5 a for t because we don't have data about the actual length of nursing.
Using this equation the estimated AUCs for Finnish children (4, 12, and 20 pg*a/g for groups <8.0, 8.016, and >16) result in longterm intakes of 38, 114, and 190 pg/d, respectively. Therefore, we can use these values in a regression analysis to find a doseresponse between mother's longterm daily intake of dioxin and probability of tooth defect. The linear slope from the highest and lowest group is (0.25  0.04)/(19038) = 0.001382 (pg/d)^{1}.
PCB and cancer
Upper bound slope factor  Centralestimate slope factor  
High risk and persistence  2.0  1.0 
Low risk and persistence  0.4  0.3 
Lowest risk and persistence  0.07  0.04 
In Beneris slope factor of 2 is used.
ERF of PCB on cancer indexed by variable age. It applies to adults, i.e. > 18 years old (gender combined).
The U.S. Environmental Protection Agency (US EPA) recommends using cancer slope factors (CSFs) when evaluating potential cancer risks of PCB mixtures.^{[16]} There are three tiers of CSFs for environmental PCBs that depend on the exposure pathway. These are: high risk and persistence, low risk and persistence, lowest risk and persistence. In each of these tiers EPA reports central and upper bound estimate of CSF. In general, central estimate slope factors are used to estimate a typical individual’s risk while upperbound slope assure that this risk is not likely to be underestimated if the underlying model is correct.
According to the US EPA exposures via food chain are associated with the highest risk and persistence. Therefore CSFs from the first tier are recommended to be used when estimating cancer risks from food chain pathways.
PCB and stroke
During followup 1386 incident cases of myocardial infarction were ascertained through registerlinkage. Women in the highest quartile of dietary PCB exposure (median 286 ng/day) had a multivariableadjusted RR of myocardial infarction of 1.21 (95% confidence interval [CI], 1.011.45) compared to the lowest quartile (median 101 ng/day) before, and 1.58 (95% CI, 1.102.25) after adjusting for EPADHA. Stratification by low and high EPADHA intake, resulted in RRs 2.20 (95% CI, 1.184.12) and 1.73 (95% CI, 0.813.69), respectively comparing highest PCB tertile with lowest. The intake of dietary EPADHA was inversely associated with risk of myocardial infarction after but not before adjusting for dietary PCB. ^{[17]}
Case: municipal solid waste incinerator
A case study was performed for a municipal solid waste incinerator plan in Hämeenkyrö, Finland, in 2006. The case has a detailed assessment page in Finnish: Hämeenkyrön jätteenpolttolaitos. Here, only a few key points are raised as an example of dioxin risk assessment.
 MSWI is likely to increase background dioxin exposure (additional low exposure).
 The risk of accidental exposure is low (dioxin emissions will increase only if burning process is working improperly).
 Health effects of longterm exposure are relevant.
 Effects on development and endocrine functions are more relevant than the risk of cancer.
 The health effects of low doses should be modelled from animal and human data. Eg. Alaluusua et al. (1996) have studied tooth development. In a study by Miettinen et al. (2005)^{[13]}, exposure to 0.5 μg TCDD/kg body weight on GD 15 resulted in maternal adipose tissue concentration of 2185 pg/g fat. In that study, linear extrapolation of the data predicts a maternal adipose tissue concentration of 100120 pg/g fat after exposure to 0.03 μg TCDD/kg body weight. This estimated maternal adipose tissue concentration is sufficient to induce developmental dental defects in rat offspring, and is similar to the highest values measured in the Finnish average population (PCDD/F 145.5 pg WHOTEQ/g fat (Kiviranta et al. 2005).
Sensitive subgroups: foetuses, newborns, young females (women below or at childbearing age), individuals with high fish consumption (e.g. fishermen), individuals working in incineration plants etc.
Tolerable daily intake (TDI): 14 pg/kg body weight
Calculations
<rcode name="ERF_diox2" label="Initiate ERF" embed=1>
 This is code Op_en5823/ERF_diox2 on page ERF of dioxin
 Note! This version has ERF and threshold in the same ovariable.
library(OpasnetUtils)
ERF_diox < Ovariable("ERF_diox", ddata = "Op_en5823") colnames(ERF_diox@data) < gsub(" ", "_", colnames(ERF_diox@data))
objects.store(ERF_diox) cat("Ovariable ERF_diox stored.\n") </rcode>
<rcode name="initiate" label="Initiate ovariables" embed=1 store=1> library(OpasnetUtils)
d < opbase.data("Op_en5823") d$Obs < NULL colnames(d) < gsub(" ", "_", colnames(d)) d$Result < ifelse(d$Result == "", "0", as.character(d$Result))
ERF_diox < Ovariable("ERF_diox", data = d[d$Observation == "ERF", colnames(d) != "Observation"])
threshold_diox < Ovariable("threshold_diox", data = d[d$Observation == "Threshold", colnames(d) != "Observation"])
objects.store(ERF_diox, threshold_diox) cat("Ovariables ERF_diox, threshold_diox stored.\n") </rcode>
See also
 Dioxin
 PCDD/F
 2,3,7,8Tetrachlorodibenzopdioxin CASRN 1746016  IRIS  US EPA, ORD
 Exposure and Human Health Reassessment of 2,3,7,8TetrachlorodibenzoPDioxin (Tcdd) and Related Compounds National Academy Sciences (External Review Draft) (2004)  Risk Assessment Portal  US EPA
References
 ↑ EC Scientific Committee on Food. (2001) Opinion of the Scientific Committee on Food on the risk assessment of dioxins and dioxinlike PCBs in food. CS/CNTM/DIOXIN/20 final [1]
 ↑ Mocarelli P et al. Dioxin exposure, from infancy to puberty, produces endocrine disruption and affects human semen quality. Environmental Health Perspectives 2008: 116(1)
 ↑ Mocarelli P. et al. Perinatal exposure to low doses of dioxin can permanently impair human semen quality. Environmental Health Perspectives 2011: 119(5).
 ↑ MinguezAlarcon L. et al. A longitudinal study of peripubertal serum organochlorine concentrations and semen parameters in young men: the Russian children's study. Environmental Health Perspectives 2017: 125(3).
 ↑ Sharpe1, RM. Sperm counts and fertility in men: a rocky road ahead. EMBO Rep. 2012 May; 13(5): 398–403. doi:10.1038/embor.2012.50 [2].
 ↑ Exposure and Human Health Reassessment of 2,3,7,8TetrachlorodibenzoPDioxin (Tcdd) and Related Compounds National Academy Sciences (External Review Draft) (2004) [3]
 ↑ U.S.EPA. Information Sheet 2, Dioxin: Scientific Highlights from the NAS Review Draft of EPA’s Dioxin Reassessment [4]
 ↑ Tuomisto J, Pekkanen J, Kiviranta H, Tukiainen E, Vartiainen T, Viluksela M, Tuomisto JT. Dioxin Cancer Risk  Example of Hormesis? Dose Response. 2006 May 1;3(3):332341.
 ↑ ^{9.0} ^{9.1} Alaluusua, S., Calderara, P., Gerthoux, P.M., Lukinmaa, PL., Kovero, O., Needham, L., Patterson, D.G., Tuomisto, J., and Mocarelli, P. (2004) Developmental dental aberrations after the dioxin accident in Seveso. Environ Health Perspect. 112, 13138.
 ↑ PL Gradowska PhD thesis 2013
 ↑ Kattainen, H., Tuukkanen, J., Simanainen, U., Tuomisto, J.T., Kovero, O., Lukinmaa, PL., Alaluusua, S., Tuomisto, J., and Viluksela, M. (2001) In utero/lactational 2,3,7,8tetrachlorodibenzopdioxin exposure impairs molar tooth development in rats. Toxicol Appl Pharmacol. 174, 21624.
 ↑ Alaluusua et al. Eur J Oral Sci. 1996 OctDec;104(56):4937.
 ↑ ^{13.0} ^{13.1} Miettinen HM et al. Toxicol Sci. 2005 Jun;85(2):100312.
 ↑ Alaluusua S, Lukinmaa PL, Vartiainen T, Partanen M, Torppa J, Tuomisto J. Polychlorinated dibenzopdioxins and dibenzofurans via mother's milk may cause developmental defects in the child's teeth. Environ Toxicol Pharmacol. 1996 May 15;1(3):1937. [5]
 ↑ Alaluusua S, Lukinmaa PL, Torppa J, Tuomisto J, Vartiainen T. Developing teeth as biomarker of dioxin exposure. Lancet. 1999 Jan 16;353(9148):206. [6]
 ↑ IRIS. US EPA. http://www.epa.gov/iris/subst/0294.htm
 ↑ Bergkvist C, Berglund M, Glynn A, Wolk A, Åkesson A. Dietary exposure to polychlorinated biphenyls and risk of myocardial infarction  a populationbased prospective cohort study. Int J Cardiol. 2015 Mar 15;183:2428. doi: 10.1016/j.ijcard.2015.01.055. [7]
 Is the fear of dioxin cancer more harmful than dioxin? Jouko Tuomisto, Jouni T. Tuomisto: Toxicology Letters 2012.
 Alaluusua et al ETAP 1996: Polychlorinated dibenzopdioxins and dibenzofurans via mother's milk may cause developmental defects in the child's teeth.
 Lancet. 1999 Jan 16;353(9148):206.
Developing teeth as biomarker of dioxin exposure. Alaluusua S, Lukinmaa PL, Torppa J, Tuomisto J, Vartiainen T.
 Mocarelli et al EHP 2008: Dioxin Exposure, from Infancy through Puberty, Produces Endocrine Disruption and Affects Human Semen Quality
 Alaluusua et al 2004: Developmental dental aberrations after the dioxin accident in Seveso.
 Crump et al. 2003. Metaanalysis of dioxincancer doseresponse for three occupational cohorts. Environmental Health Perspectives 111 (5), 681687.
 Kiviranta et al. Chemosphere. 2005 Aug;60(7):85469.
 Kogevinas 2001. Human health effects of dioxins: cancer, reproductive and endocrine system effects. Human Reproduction Update 7 (3), 331339.
 Tuomisto JT et al. Int J Cancer. 2004 Mar 1;108(6):893900.
 Tuomisto et al. 1999. Synopsis on dioxins and PCBs. Publications of the National Public Health Institute B17/1999.
 van Leeuwen FX et.al. Chemosphere. 2000 MayJun;40(911):1095101.