ERF of dioxin

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Citation of this page: Jouni T. Tuomisto, Päivi Meriläinen: Dioxin exposure-response function on population level. Opasnet 2009. [8]. Accessed 24 Nov 2024.
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ERF of dioxin describes quantitative relationships between exposure to polychlorinated dibenzo-p-dioxins, 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 dibenzo-p-dioxins, polychlorinated dibenzofurans (PCDD/Fs), and polychlorinated biphenyls (PCBs)) and several health impacts including

• remaining lifetime cancer risk and
• developmental defects in molar teethR↻

Answer

+ Show code- Hide code library(OpasnetUtils) objects.latest("Op_en5823", code_name = "initiate") # [[ERF of dioxin]] ovariables ERF, threshold. oprint(summary(EvalOutput(ERF_diox)))

Rationale

Dioxins are a group of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). They are persistent environmental contaminants that accumulate in the human body. Their elimination half-life is quite high (~7 years). 2,3,7,8-tetrachlorodibenzo-p-dioxin (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 long-term 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.

ERF of dioxin: Difference between revisions(-)

Obs	Exposure agent	Response	Exposure	Exposure unit	ER function	Scaling	Threshold	ERF	Description
1	TEQ	Yes or no developmental dental defects incl. agenesis	Ingestion etc. (as it was in Seveso) as log(TCDD serum concentration+1) in fat	log(pg /g)	ERS	Log10	0	0.26 +- 0.12	Alaluusua 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)
2	TEQ	Yes or no tooth defect	Intake through placenta and mother's milk as log(TCDD serum concentration+1) in fat	log(pg /g)	ERS	Log10	0	0:0.06:0.12	Alaluusua et al. 2004 data with PL Gradowska PhD thesis 2013 approach but we used the response function y = k x + b (see below)
3	TEQ	Yes or no dental defect	Intake through placenta and mother's milk	pg /g	ERS	None	0	0:0.001382:0.0028	Alaluusua 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
4	TEQ	Cancer morbidity	Ingested intake	pg /kg /d	CSF	BW	0	0 - 0.001	U.S.EPA 2004: 10E-3 (pg/kg/d)^-1. Lifetime probability
5	TEQ	Cancer morbidity yearly	Ingested intake	pg /kg /d	CSF	BW	0	0 - 0.00002	U.S.EPA 2004: 10E-3 (pg/kg/d)^-1. Lifetime probability divided by 50 to produce average yearly risk
6	TEQ	Dioxin recommendation tolerable daily intake	Ingested intake	pg /kg /d	TDI	BW	0	2	TWI 14 pg/kg/week by SCF (2001).
7	TEQ	Dioxin recommendation tolerable daily intake 2018	Ingested intake	pg /kg /d	TDI	BW	0	0.2889	TWI 2 pg/kg/week by EFSA committee (2018).
8	TEQ	Sperm concentration	Intake through placenta and mother's milk	pg /g	ERS	None	0	0.00006+-0.00004	Minguez-Alarcon 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.

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.

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 and a Russian children's study.

EFSA recently assessed this risk and derived dose-response 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 50-80) million/ml in the lowest quartile, while in all other quartiles the concentration was 40 (96 % CI 30-55) million/ml. (Numbers are approximate because they have been read from a figure.)

According to a review, 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 exposure-response function of dioxin on sperm concentration that has the non-linear 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 6-month periods), the probability of not getting pregnant follows this curve:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P(infertility after 5 a) = (1 - 0.65 (1+ \frac{-0.39c}{c + 10.5 pg/g})^{10},}

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, 0-5 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 exposure-response is non-linear, 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.

+ Show code- Hide code # 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=(1-0.65*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)")

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). 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 dose-response is suspected, 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.

Exposure-response functions for tooth defects caused by TCDD (study-specific) describes study-specific exposure-response 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)

• 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 (0-2 and 2-18 years, gender combined). It has been agreed that this ERF can be applied to WHO-TEQ concentration of dioxin (PCDD/F) and dioxin-like PCB in body fat.

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. 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 (%)
non-ABR zone	39	10		40.5	26
Exposed group 1	10	1	31-226	128.5	10
Exposed group 2	11	5	238-592	415	45
Exposed group 3	15	9	700-26000	3000	60

It has been assumed that the TCDD exposure in children from the non-ABR 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 log-uniformly distributed over the range of serum levels reported in the table. The log-logistic model (with constant term) was chosen to model the relation between the log-transformed 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.

1. Sample ni exposures from distribution of TCDD serum level in group i. Denote these exposures as xj, j=1,...,75.
2. Assign ri responses randomly to ni people.
3. Fit the dose-response model to simulated data, call it model p0.
4. Compute predicted probability for every person in the study, i.e. compute p0(xj).
5. Re-sample the response of each person assuming that the probability that person j responds is p0(xj), j=1,...,75.
6. Re-fit the dose-response model.
7. Iterate steps 5-6 100 times.
8. Repeat steps 1 - 7 1500 times.
9. Create the density histogram of simulated estimates of regression coefficient (only positive values are kept).
10. 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 non-dioxin-related 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. They estimated dioxin exposure by using area under curve:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle AUC = \frac{C (1 - e^{-k_e t})}{k_e},}

where
AUC = area under the curve (pg a /g)
C = concentration in mother's milk (pg /g)
ke = mother's elimination rate for dioxin during lactation (0.2877 /a)
t = time of nursing (a)

Mineralization defects of the permanent first molars.

Outcome	Number of children with exposure (pg*year/g milk fat)
	Low exposure (<8.0)	Moderate exposure (8.0-16)	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.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C_{s,m} = \frac{I_{a,m} * t_{1/2,m} * f_m}{ln2 * BF_i},}

where
Cs,m = dioxin concentration in serum (or fat or milk) in the mother (pg/g fat)
Ia,m = average daily intake of dioxin of the mother in absolute amounts pg/day
t1/2,m = the half-life of dioxin in the mother when not nursing (2737.5 d = 7.5 a)
fm = fraction of ingested dioxin actually absorbing from the gut in the mother (0.80)
BFm = 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:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle I_{a,m} = \frac{ln2 * BF * AUC * k_e}{t_{1/2,m} * f_m (1 - e^{-k_e t)}},}

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, 8.0-16, and >16) result in long-term intakes of 38, 114, and 190 pg/d, respectively. Therefore, we can use these values in a regression analysis to find a dose-response between mother's long-term daily intake of dioxin and probability of tooth defect. The linear slope from the highest and lowest group is (0.25 - 0.04)/(190-38) = 0.001382 (pg/d)^-1.

PCB and cancer

Cancer slope factors for PCBs (mg/kg bw/d)^-1

	Upper bound slope factor	Central-estimate 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. 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 upper-bound 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 follow-up 1386 incident cases of myocardial infarction were ascertained through register-linkage. Women in the highest quartile of dietary PCB exposure (median 286 ng/day) had a multivariable-adjusted RR of myocardial infarction of 1.21 (95% confidence interval [CI], 1.01-1.45) compared to the lowest quartile (median 101 ng/day) before, and 1.58 (95% CI, 1.10-2.25) after adjusting for EPA-DHA. Stratification by low and high EPA-DHA intake, resulted in RRs 2.20 (95% CI, 1.18-4.12) and 1.73 (95% CI, 0.81-3.69), respectively comparing highest PCB tertile with lowest. The intake of dietary EPA-DHA was inversely associated with risk of myocardial infarction after but not before adjusting for dietary PCB.

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 long-term 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), 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 100-120 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 WHO-TEQ/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): 1-4 pg/kg body weight

Calculations

+ Show code- Hide code # 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")

+ Show code- Hide code 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")

See also

• Dioxin
• PCDD/F
• 2,3,7,8-Tetrachlorodibenzo-p-dioxin CASRN 1746-01-6 | IRIS | US EPA, ORD
• Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-P-Dioxin (Tcdd) and Related Compounds National Academy Sciences (External Review Draft) (2004) | Risk Assessment Portal | US EPA

References

• Is the fear of dioxin cancer more harmful than dioxin? Jouko Tuomisto, Jouni T. Tuomisto: Toxicology Letters 2012.
• Alaluusua et al ETAP 1996: Polychlorinated dibenzo-p-dioxins 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. Meta-analysis of dioxin-cancer dose-response for three occupational cohorts. Environmental Health Perspectives 111 (5), 681-687.
• Kiviranta et al. Chemosphere. 2005 Aug;60(7):854-69.
• Kogevinas 2001. Human health effects of dioxins: cancer, reproductive and endocrine system effects. Human Reproduction Update 7 (3), 331-339.
• Tuomisto JT et al. Int J Cancer. 2004 Mar 1;108(6):893-900.
• 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 May-Jun;40(9-11):1095-101.

Related files

• Alaluusua ETAP1996 mother's milk and child's tooth defect