Apparent Half-Lives of Dioxins, Furans, and Polychlorinated Biphenyls as a Function of Age, Body Fat, Smoking Status, and Breast-Feeding

Objective In this study we reviewed the half-life data in the literature for the 29 dioxin, furan, and polychlorinated biphenyl congeners named in the World Health Organization toxic equivalency factor scheme, with the aim of providing a reference value for the half-life of each congener in the human body and a method of half-life estimation that accounts for an individual’s personal characteristics. Data sources and extraction We compared data from > 30 studies containing congener-specific elimination rates. Half-life data were extracted and compiled into a summary table. We then created a subset of these data based on defined exclusionary criteria. Data synthesis We defined values for each congener that approximate the half-life in an infant and in an adult. A linear interpolation of these values was used to examine the relationship between half-life and age, percent body fat, and absolute body fat. We developed predictive equations based on these relationships and adjustments for individual characteristics. Conclusions The half-life of dioxins in the body can be predicted using a linear relationship with age adjusted for body fat, smoking, and breast-feeding. Data suggest an alternative method based on a linear relationship between half-life and total body fat, but this approach requires further testing and validation with individual measurements.

Polychlorinated dibenzopdioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls (PCBs) are lipophilic and can persist in the body for years (Schecter et al. 2006). An indi vidual's body burden is a product of multiple years of exposure (Pinsky and Lorber 1998) and a lifetime of varying elimination rates. Different congeners of PCDDs, PCDFs, and PCBs each have different persistence in the human body, reflected in their different reported halflives. The apparent halflife, defined as the change in concentration in the body over time, is the net result of elimina tion from the body, changes in body com position, and intake from the environment. For each congener, variation in halflife exists both among individuals and within the same individual over his or her lifetime. This varia bility can be partially attributed to personal characteristics, including age, body fat, smok ing status, and breastfeeding. The factors that affect elimination rates must be taken into account when predicting past exposures and body burdens of these chemicals and when comparing current serum congener profiles to exposure media.
Age. In a study of German chemical work ers, halflives of numerous dioxins and furans were positively associated with increasing age (FleschJanys et al. 1996). This is consistent with a study on the Yusho and YuCheng cohorts of halflives of penta (Pe), hexa (Hx), and hepta (Hp) CDFs (Leung et al. 2007).
Studies on the Ranch Hand cohort show a slight negative association (Wolfe et al. 1994) or no association (Michalek et al. 1996), but this may be due to the narrow age range char acterizing these cohorts. Studies with child or infant subjects report significantly shorter halflives than do studies with adult cohorts (Kreuzer et al. 1997;Leung et al. 2006Leung et al. , 2007. In children < 18 years of age exposed during the incident in Seveso, Italy, a strong associa tion between halflife and age was found, and children had significantly shorter halflives than did adults (Kerger et al. 2006).
The rapid growth of neonates and children, especially in lipid stores, can result in a dra mat ically reduced apparent halflife through dilu tion (Clewell et al. 2004). However, the effect of dilution alone is not sufficient to create the observed reduction in apparent halflife; it may also be due to a faster metabolism, an increased rate of fecal lipid excretion, or a combination of these events (Abraham et al. 1996;Kerger et al. 2007b). As children age, their rate of growth slows, and the effect of elimination on apparent halflife becomes more important than that of dilution.
The relationship between age and halflife is complex because age is strongly associated with other factors that affect halflife length (e.g., smoking status, percent body fat). As humans age, they generally experience an increase in and a redistribution of body fat as well as a relative change in organ sizes, causing a redistribution of lipophilic chemicals that greatly alters their rates of elimination (Van der Molen et al. 1996). Additionally, age may have an independent effect through an agerelated reduction in hepatic elimination capacity (Aylward et al. 2005). A strong cohort effect is seen in crosssectional studies, caused by varying levels of persistent chemicals in the environment. During the 1960s and 1970s, environmental levels of dioxins were much higher than they are today, leading to higher body burdens of the more persistent congeners in older people, above the level expected from persistence alone (Pinsky and Lorber 1998).
Smoking status. Smoking has been associ ated with lower levels of dioxins and dioxin like compounds. Active smokers have lower PCDD, PCDF, and PCB serum levels than do both non smokers and passive smokers (Brown and Lawton 2001;Chen et al. 2005), and levels of dioxinlike PCBs in human milk are negatively related to the smoking habits of the mothers (Uehara et al. 2007). This is in agreement with results of Flesch Janys et al. (1996), who observed that the halflives of some PCDD and PCDF conge ners appeared to be dependent on smoking status. They observed a significantly faster decay in smokers, with increases ranging from 30% [2,3,7,8tetra chlorodibenzopdioxin (TCDD)] to 100% 1,2,3,4,7,8HxCDD.
Smoking induces the transcription of cyto chrome P450 (CYP) 1A2 and other enzymes responsible for the elimination of dioxin and dioxinlike compounds, most likely through the activation of the aryl hydrocarbon oBjective: In this study we reviewed the half-life data in the literature for the 29 dioxin, furan, and polychlorinated biphenyl congeners named in the World Health Organization toxic equivalency factor scheme, with the aim of providing a reference value for the half-life of each congener in the human body and a method of half-life estimation that accounts for an individual's personal characteristics. data sources and extraction: We compared data from > 30 studies containing congener-specific elimination rates. Half-life data were extracted and compiled into a summary table. We then created a subset of these data based on defined exclusionary criteria. data synthesis: We defined values for each congener that approximate the half-life in an infant and in an adult. A linear interpolation of these values was used to examine the relationship between half-life and age, percent body fat, and absolute body fat. We developed predictive equations based on these relationships and adjustments for individual charac teristics. conclusions: The half-life of dioxins in the body can be predicted using a linear relationship with age adjusted for body fat, smoking, and breast-feeding. Data suggest an alternative method based on a linear relationship between half-life and total body fat, but this approach requires further testing and validation with individual measurements.  (Zevin and Benowitz 1999). The total effect of smoking on halflife may be through this increased induction of dioxin degrading enzymes, or through a combination of other physiologic effects.
Body burden. Dioxins are known to upregulate the enzymes responsible for their own elimination. Modeled and experimental data in rats show that at high exposures the induction of CYP1A2 is a more important factor for persistence in the body than are dif ferences in adipose tissue distribution (Emond et al. 2006). A concentrationdependent bipha sic elimination rate has been identified in cases of acute poisoning , in the Seveso incident (Aylward et al. 2005;Michalek et al. 2002), in children (Kerger et al. 2006), and in the Yusho and YuCheng rice oil poisonings (Leung et al. 2007;Ryan et al. 1993). Human data suggest that the serum concentration where this transition occurs is 700 ppt (Kerger et al. 2006) for TCDD and 1,000-3,000 ppt for PCDFs (Leung et al. 2005). These concentrations are considerably higher than those measured in people exposed to present background levels.
Body fat. Because dioxins, furans, and PCBs are highly lipophilic, they partition pref erentially in adipose tissue and other body fat. High amounts of adipose tissue, estimated by body mass index [BMI; weight (kilograms)/ height 2 (meters)], are associated with higher serum levels of dioxins and furans (Collins et al. 2007). Because adipose tissue acts as a reservoir for these chemicals, increases in adi pose tissue result in their storage rather than transportation to excretory and metabolizing organs. Models based on the rat data demon strate a linear relationship between increas ing fat mass and halflife length at low body burdens, with the impact of adipose tissue on halflife becoming less important at high body burdens (Emond et al. 2006).
The relationship between percent body fat and halflife is apparent throughout the Ranch Hand study (Michalek et al. 1992(Michalek et al. , 1996Michalek and Tripathi 1999), but these studies did not find a significant relationship between halflife and shortterm changes in percent body fat. These findings are supported by the German occupational cohort, where a 1% increase in percent body fat was associ ated with a decay rate decrease in the range of 0.0031 ng/kg/year (1,2,3,6,7,8HxCDD) to 0.0063 ng/kg/year (1,2,3,4,6,7,8HpCDD) for dioxins, and about 0.005 ng/kg/year for furans (FleschJanys et al. 1996). This study did show an increased decay rate in workers with intermediate weight loss, but in a limited number of people (n = 3). Halflife is moder ately correlated with both BMI and body fat mass in children, but longitudinal data from children are difficult to interpret because of their fast growth and simultaneous agerelated changes (Kerger et al. 2006).
Breast-feeding. For women, lactation can be the major route of elimination of many persis tent lipophilic chemicals (Abraham et al. 1996;Schecter et al. 1996). Twenty percent or more of the maternal body burden of some persistent pollutants, such as PCBs, can be transferred during 6 months of lactation (Landrigan et al. 2002;Niessen et al. 1984). The reduction of halflife due to breastfeeding is both congener specific and duration dependent. The amount of fat in breast milk varies over time, affecting the partitioning of chemicals from the body (Clewell and Gearhart 2002). Different con geners partition differently into the breast milk from the blood (Schecter et al. 1996(Schecter et al. , 1998, and this distribution is thought to be depen dent on the molecular weight of the congener. Along with molecule diameter and differences in lipophilicity, molecular weight may influ ence membrane permeability, thus causing dif ferences in distribution (Wittsiepe et al. 2007).
Although studies show an association between individual characteristics and the pharmacokinetics of dioxins, furans, and PCBs in the human body, there is no stan dard method for determining a chemical's halflife as a function of these factors. Most halflife studies for dioxins, furans, and PCBs follow accidental or occupational exposures, and no single study exists covering the life span of people with varying physical charac teristics. Despite summaries of pharmaco kinetic data of dioxins, furans (Ogura 2004), and PCBs (Lotti 2003), estimations of expo sure and body burden have been hindered by the absence of a halflife range and value for each congener.

Materials and Methods
We conducted an extensive literature search for human halflife or decay values for the 29 con geners of dioxins, furans, and dioxinlike PCBs included in the World Health Organization 2005 toxic equivalency factor (TEF) scheme (Van den Berg et al. 2006). Measured or mod eled halflife values for each congener and the age of the subject or mean age of the cohort were recorded from > 30 studies (Tables 1-4).
We selected a subset of data based on the following criteria: blood serum concentrations < 700 ppt total toxic equivalents (TEQs) at the time of sampling, adult subjects, and meas ure ments not reported as inaccurate in later studies. We retained halflife values that were calculated assuming steadystate condi tions if they were < 25 years, because this assumption is inappropriate for more per sistent substances with significantly higher historical levels. The mean and range of half lives were calculated for the retained subset to establish a representative set of halflives for each congener in a moderately exposed adult.
We selected the adult reference values to represent a 40 to 50yearold with blood dioxin concentrations in the range where fat drives the rate of elimination. We preferen tially chose sources that provided consistent data across congeners and that were within the range of all measured data. Infant refer ence values were chosen to represent an indi vidual < 2 years of age. When infant data were not available, we multiplied the adult reference value for the congener by the ratio of the length of the adult halflife over the infant halflife for TCDD.
We examined halflife variation as a func tion of individual characteristics. When the mean age of the cohort was not explicitly pro vided, we estimated the mean age at the mid point of sampling. When percent body fat or total body fat data were not available, we converted the mean agespecific BMI reported in the National Health and Nutrition Examination Survey (NHANES) 2003-2004 study [Centers for Disease Control and Prevention (CDC) 2006] to percent body fat. For adults, we used the approach proposed by Deurenberg et al. (1991): where sex corresponds to females = 0, and males = 1. We used this approach in adults because, unlike the method developed by Knapik et al. (1983) that is used by Flesch Janys et al. (1996) and the Ranch Hand cohort analysis (Michalek et al 1996;Wolfe et al. 1994), it takes into account both age and sex. Studies have shown that if age is not included in the conversion from BMI to per cent body fat, it may seriously underestimate Values shown are parametric estimates except where indicated. ∞ (Infinity) indicates that at least one person had an increase in serum concentrations between measurements. a Values that fit exclusionary criteria for the subset. b Value not defined. c As reported in Ogura (2004). d Application of model in Ogura (2004). e Also reported a parametric estimate of 7.1 for 1,2,3,7,8,9-HxCDF. percent body fat in older people (Deurenberg et al. 1991;Hattis et al. 2003).
In children (0-19 years of age), we used a series of agebased equations presented by Hattis et al. (2003) to predict percent body fat for each age in months. Total body fat was estimated by multiplying the average weight reported in the NHANES data for a given age and sex by the calculated percent body fat (CDC 2006).
Based on the apparent relationships between halflife and these parameters, we propose a procedure of halflife estimation that is a function of age, percent body fat, smoking status, and breastfeeding.

Review of reported half-life values.
A compre hensive report of halflife values for dioxins, furans, and PCBs is presented in Tables 1-4. Studies that are listed more than once are those that report multiple halflife values, gen erally corresponding to measurements on dif ferent individuals. Of the studies examined, onethird are limited to TCDD: five of these report on the Ranch Hand cohort (Michalek et al. 1996(Michalek et al. , 2002Michalek and Tripathi 1999;Pirkle et al. 1989;Wolfe et al. 1994), three with kinetic data based on the incident in Seveso, Italy (Kerger et al. 2006;Michalek et al. 2002;Needham et al. 1997), one on a poisoning incident in Austria , and two based on an adult male volun teer (Poiger and Schlatter 1986;Schlatter 1991). Sixteen different measurements are based on the YuCheng and Yusho cohorts (Chen et al. 1982;Kashimoto et al. 1983;Leung et al. 2005Leung et al. , 2007Masuda 1989, 1991;Ryan et al. 1993;Shirai and Kissel 1996). Six studies report models or measurements based on occupational expo sures (Brown et al. 1989;FleschJanys et al. 1996;Rohde et al. 1999;Schecter et al. 1990; Van der Molen et al. 2000;Wolff et al. 1992). Five studies have information only on infants and children (Gorski et al. 1984;Kerger et al. 2007aKerger et al. , 2007bKreuzer et al. 1997;Leung et al. 2006;Wolff and Schecter 1991), and two data sets are based on general populations (Ogura 2004). The average number of values for dioxins and furans is 10, and among the PCBs the average is 4. No halflife data were available for 1,2,3,7,8,9HxCDF.
The ranges of the subsets of reported val ues for adults are shown in Figure 1 (dioxins and furans) and Figure 2 (PCBs), and the val ues are shown in Tables 1-3. The compari son of reported halflife values reveals large variation across congeners. For example, the mean halflives of octachlorinated dibenzo furan (octaCDF), tetrachlorinated dibenzo furan (TCDF), and 1,2,3,7,8PeCDF are all < 3 years, whereas the mean halflives for some of the HxCDD congeners are more than a decade. The halflives in the PCBs range from only a few months (PCB77) to a few decades (PCB157), and one study reported a > 100 fold range in metabo lic clearance rates between PCB congeners (Brown and Lawton 2001).
Within each congener, halflife values reported from the literature vary substantially, typically by a factor of 2-3, but up to a factor of 35 within the subset. This variation may be a result of different exposure concentrations or scenarios, differences in the demographics of the considered cohort, or differences in the pharmaco kinetic model used in halflife calcu lation. Several studies reported on a single per son or had very small sample sizes, resulting in unstable mean values. For example, the 15.7 year halflife reported by FleschJanys et al. (1996) for 1,2,3,7,8PeCDD became 11 years when they excluded one worker close to back ground. Some of the variability in reported halflife values can be explained through dif ferences in physiologic processes among indi viduals and different congener properties. However, very short halflives (i.e., < 1 year) are unlikely for the most frequently found con geners because of the high exposures required to sustain measurable body burdens, and very long halflives (> 10 years) may be artifacts of ongoing exposures (Shirai and Kissel 1996).
Most cohorts consist of adult males exposed to high concentrations, although mea surements were sometimes carried out years after exposure. Halflife meas ure ments for per sons at or near background levels, including those with no history of substantial exposure or those who have returned to background levels after significant exposure, may be confounded by the effect of probable continuous exposure to background levels of dioxins. Halflife mea surements and the influence of other factors (e.g., smoking, body fat) may be better evalu ated when sampled from persons with higher accidental exposures, if concentrationdepen dent effects can be clearly accounted for. Most of the studies report concentrations normalized by gram of lipid and assume a con served equilibrium between dioxins and lipids across the body. The suitability of this mea surement to calculate the overall body burden depends on the distribution of the congener into adipose tissue. Although different conge ners partition differently into different organs (Iida et al. 2007;Kitamura et al. 2001), a cor relation between levels in the blood and levels in adipose tissue is supported (Iida et al. 1999).
Variation in half-life as a function of age. We observed a positive association between age and halflife ( Figure 3). Although this may indicate a direct relationship between age and halflife, it also incorporates the effect of other parameters, such as agerelated changes in percent body fat. We included the influ ence of body fat, using BMI as a surrogate, in the displayed regressions, which use the mean agespecific BMI reported for the 2003-2004NHANES study (CDC 2006. The points representing literaturereported data in Figures 3-6 are generally averages of a range of ages and a range of halflife values. These ranges, where available, are presented in Tables 1-3. Application of the model pro posed by Van der Molen et al. (2000) results in nonlinear variations at low ages. These variations are linked to modeled variations in body fat during adolescence, but have not been confirmed by experimental data.
The Kerger et al. (2006) data correspond to children with concentrations < 700 ppt and support the hypothesis of a close to linear increase in halflife between ages 0-35 years. The slopes calculated with this method were similar to slopes for adults calculated with the method provided by FleschJanys et al. (1996), spanning adults 30-80 years of age. However, the equation proposed by FleschJanys et al. (1996) may be problema tic for ages > 60 years because very small variations in the elimina tion rate could lead to substantial divergence in halflife length, as observed in the case of 1,2,3,7,8PeCDD ( Figure 4).
Overall, we observed a nearly linear asso ciation between halflife and age, which is most likely linked to the combined effects of growthcaused dilution at young ages and an increase in body fat at older ages. However,  (1996). e Age in 1982. f Age during tour of duty. g Also published by Ryan and Masuda (1991). h Data accessed from U.S. Environmental Protection Agency (2000). i Application of model presented to data from study in Chen et al. (1982). j Data accessed from Ryan et al. (1993). k Data accessed from abstract. l Reported two metabolic clearance rates, not apparent half-life values; clearance rates were assumed to be additive, and half-lives were calculated as follows: t 1/2 = 1/k a + 1/k b . m Did not account for growth; may be near background. n Data accessed from Shirai and Kissel (1996). o Application of kinetic model to data.
volume 117 | number 3 | March 2009 • Environmental Health Perspectives this association does not account for inter individual variation at each age.

Variation of half-life with body fat.
Percent body fat is a good predictor of halflives in adults, as shown for TCDD in Figure 5. This method is inappropriate for infants and children (identified by oval in figure) because of drastic changes in percent body fat and short halflives.
The discrepancy between percent body fat and halflives observed at young ages sug gests the use of absolute body fat mass to account for the effect of fat over the entire age range (Figure 6). We obtained total body fat by multiplying calculated percent body fat by agespecific NHANES weight aver ages (CDC 2006). Further data collection is needed to confirm the validity of the relation ship between body fat mass and halflife.
Reference half-life values. We preferentially used the regression method used by Flesch Janys et al. (1996) for adult reference halflife values because it covers multiple congeners in a consistent way and incorporates infor mation for percent body fat, sex, and smok ing status, and because the resulting values are within the range of the other values in the literature. In the case of TCDD, we used the single median value given by FleschJanys et al. (1996) as the reference value, because of its consistency with other reported data. For dioxin and furan congeners not reported by FleschJanys et al. (1996), we used the model proposed by Van der Molen et al. (2000) to determine a reference halflife, using the median age (48.7 years) and percent body fat (21.9%) from FleschJanys et al. (1996). For 1,2,3,7,8,9HxCDF, which had no available halflife data, we used the reference halflife for 1,2,3,6,7,8HxCDF.
We based reference halflives of PCB77 and PCB81 on measurements from samples of adipose tissue, whereas we determined ref erence halflives for the 10 remaining PCB congeners based on measurements of blood (Ogura 2004). These values correspond to halflives observed in the general Japanese population, assuming steadystate conditions. Because of the large decrease in dioxin, furan, and PCB concentrations in the environment in the last 30 years, the steadystate assump tion is only appropriate for congeners with halflives that are significantly shorter than the time elapsed from the peak in environ mental concentrations; the halflives of more persistent congeners could be over estimated.
We based reference halflife values for infants on congenerspecific values reported by Leung et al. (2006) where available. These values are modeled estimates based on ear lier reported concentration data for PCDD and PCDF congeners in breastfed infants (Abraham et al. 1996(Abraham et al. , 1998. These reference values are based on existing data, and better numbers may be available with the generation of new data. In some cases, it may be appro priate to use the median values, also provided in Tables 5 and 6.

Methods for individual half-life calculation.
Based on the relationships discussed above, we propose two methods to predict individualized apparent halflives of dioxins, furans, and PCBs over a lifetime. We specifi cally focused on halflives resulting from mod erate levels of exposure, comparable to those resulting from the general exposure of the U.S. population. The use of a simple multi linear regression model to predict halflife as a func tion of age and BMI or body fat is problematic because data for age and BMI coefficients are lacking for several congeners, and as previously discussed, percent body fat is not a good pre dictor of halflives at young ages.
To overcome these limitations, the first method that we propose is a linear rela tionship of halflives with age. We found the slope (β age ) and the intercept [β 0(age) ] coefficients by using a linear interpolation between the infant and adult reference halflives (shown in Tables 5 and 6). We accounted for inter individual variation in body composition and smoking habits with two multiplicative factors (Equation 2). The observed linear influence, supported by modeled results (Emond et al. 2006), of the percent body fat at age = i was incorporated in the calculation by multiplying the origi nal equation by the ratio of the individual percent body fat (pbf i ) to the reference per cent body fat for that age [pbf ref(agei) ]. We determined the reference percent body fat by converting the agespecific BMI values from the NHANES data to percent body fat using the method proposed by Deurenberg et al. (1991) and presented above. Similarly, we introduced the effect of smoking through a unit less multiplicative smoking factor (SF i ). The ratios of the decay rate of smok ers to non smokers in FleschJanys et al. (1996) were used when available, ranging from 0.5 to 0.7, corresponding to a 50% to 30% reduction in halflife (Tables 5 and 6); when not available, we used the geometric mean of all available smoking factors, cor responding to a 35% reduction in halflife. We accounted for differences between sexes indirectly by the different percent body fat values for males and females at each age. The predicted halflife (years) for an individual i as a function of age, smoking status, and  1 ,2 ,3 ,7 ,8 -P e C D D 1 ,2 ,3 ,4 ,7 ,8 -H x C D D 1 ,2 ,3 ,6 ,7 ,8 -H x C D D 1 ,2 ,3 ,7 ,8 ,9 -H x C D D 1 ,2 ,3 ,4 ,6 ,7 ,8 -H p C D D O c t a C D D 2 ,3 ,7 ,8 -T C D F 1 ,2 ,3 ,7 ,8 -P e C D F 2 ,3 ,4 ,7 ,8 -P e C D F 1 ,2 ,3 ,4 ,7 ,8 -H x C D F 1 ,2 ,3 ,4 ,6 ,7 ,8 -H x C D F 1 ,2 ,3 ,7 ,8 ,9 -H x C D F 2 ,3 ,4 ,6 ,7 ,8 -H x C D F 1 ,2 ,3 ,4 ,6 ,7 ,8 -H p C D F 1 ,2 ,3 ,4 ,7 ,8 ,9 -H  Half-life (years) * P C B -8 1 P C B -1 2 6 P C B -1 6 9 P C B -1 0 5 P C B -1 1 4 P C B -1 1 8 P C B -1 2 3 P C B -1 5 6 P C B -1 5 7 P C B -1 6 7 P C B -1 8 9 * percent body fat i was calculated using the empirical model formalized by Equation 2: This equation estimates adult halflives that are comparable to those obtained with the approach proposed by FleschJanys et al. (1996) (see Supplemental Material, Figure  1; available online at http://www.ehponline. org/members/2008/11781/suppl.pdf), while extending its applicability to children and to adults > 60 years of age.
A mathematical equation describing the additional rate of elimination due to breastfeeding (Equation 3) is based on the observed effect of breastfeeding in a cohort of German women (Wittsiepe et al. 2007). According to that study, a breastfeeding woman expels an estimated 8.76 kg fat per year through lactation [q f (kg/day), 0.8 kg milk/day of average 3% lipid], and partition coefficients between blood lipid and milk fat for each congener (K BM , unitless) range from 0.5 and 4.3 (Tables 5 and 6) (Wittsiepe et al. 2007). Δt bfed (unitless) represents the fraction of the considered year during which the woman was actively breastfeeding, and pbf i (%) and BW i (kg) are the woman's per cent body fat and body weight, respectively. [3] Assuming no interaction between breast feeding and the other halflife determinants, the overall predicted apparent halflife for a woman who is actively breastfeeding is obtained by adding the effect of elimination through breastfeeding to other ageadjusted, smokingadjusted, and bodyfat-adjusted processes.
This method predicts a halflife of 4.3 years for TCDD in a 30yearold, non smoking woman with 30% body fat if she did not breastfeed that year, and a halflife of 1.8 years if she breastfed for 6 months.
The alternative proposed strategy to model congenerspecific halflives is based on an observed apparently linear relationship a Application of the model presented by Van der Molen et al. (2000) to the Flesch-Janys et al. (1996) data as done by Ogura (2004). b Values from the current literature presented in Table 1. c Linear interpolation between the infant and adult reference half-lives (slope and intercept given in Table 5).

Age (years)
Half-life (years) Kerger et al. 2007aFlesch-Janys et al. 1996Van Der Molen et al. 2000Kerger et al. 2006 Van  (1996) for ages > 60 years may be problematic because very small variations in the elimination rate could lead to substantial divergence in half-life length.
a Application of the model presented by Van der Molen et al. (2000) to the Flesch-Janys et al. (1996) data as done by Ogura (2004). b Values from the current literature presented in Table 1. c Linear interpolation between the infant and adult reference half-lives (slope and intercept given in Table 5).  Figure 5. TCDD half-life as a function of percent body fat. The oval indicates the area where the relationship of increased half-life with increased body fat does not hold; these values represent young subjects. Literature-reported data enclosed in squares indicate subjects whose half-lives were measured when they had serum concentrations that were well above the level of increased induction of degradation enzymes.
a Application of the model presented by Van der Molen et al. (2000) to the Flesch-Janys et al. (1996) data as done by Ogura (2004). b Values from the current literature presented in Table 1.  Figure 6. TCDD half-life as a function of total body fat. The two points shown in the square represent subjects whose half-lives were measured when they had serum concentrations well above the level of increased induction of degradation enzymes.
a Application of the model presented by Van der Molen et al. (2000) to the Flesch-Janys et al. (1996) data as done by Ogura (2004). b Values from the current literature presented in Table 1.
There is insufficient data to test this equa tion, so this approach requires further data collection and validation.

Conclusion
Reported halflives of dioxin and dioxinlike congeners in humans vary widely between and within different dioxin, furan, and PCB congeners. Age, a measure of body fat, smok ing habits, and breastfeeding status are strong determinants of the elimination rates observed in humans. The present study inte grates these critical factors into an empirical model that predicts the halflives of the 29 World Health Organization TEF scheme congeners over a human life span. We sup port a method of halflife estimation that is a function of age. We found a nearly linear relationship between halflife and body fat, but further study and new data are required to evaluate the validity of any estimation methods based on this approach.
Pharmacokinetic information is scarce for many PCB congeners, and many exist ing studies report on PCB mixtures rather than individual congeners. Further, many of the existing data sets do not take into account the effect of ongoing exposures to background levels. The halflife range and reference values may be refined as more congenerspecific data becomes available. Pharmacokinetic studies across multiple con geners, which take into consideration demo graphic factors, are necessary to determine accurate elimination rates. Further study into the causes of inter individual and intra individual elimination rate variability, such as the effect of genetic polymorphisms and sensitivity to known factors, would refine halflife estimation accuracy.
The equations described here represent a simple and relatively consistent approach that can be used to determine individual apparent halflives for numerous dioxin, furan, and PCB congeners. Median and reference values are representative of average behavior rather than extremes. These values cannot be used for highly exposed persons, for whom high TEQ will induce higher elimination. However, the proposed method of halflife prediction can be used to relate past and present intake to serum concentrations and is useful to under stand the effect of demographic characteristics on serum concentrations.  Leung et al. (2006). b Flesch-Janys et al. (1996), median value. c Flesch-Janys et al. (1996), regression values. d Van der Molen et al. (2000). e No data for this congener (the half-life values were taken to be the same as 1,2,3,6,7,8-HxCDF). f Geometric mean of all K BM values.   Ogura (2004) adipose tissue data. c Geometric mean of all K BM values.