Estimating serum concentrations of dioxin-like compounds in the U.S. population effective 2005-2006 and 2007-2008: A multiple imputation and trending approach incorporating NHANES pooled sample data

Assessing disparities in Americans' exposure to PCBs and PBDEs based on NHANES pooled biomonitoring data

The National Health and Nutrition Examination Survey (NHANES) has been continuously biomonitoring Americans' exposure to two families of harmful environmental chemicals: polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). However, biomonitoring these chemicals is expensive. To save cost, in 2005, NHANES resorted to pooled biomonitoring; i.e., amalgamating individual specimens to form a pool and measuring chemical levels from pools. Despite being publicly available, these pooled data gain limited applications in health studies. Among the few studies using these data, racial/age disparities were detected, but there is no control for confounding effects. These disadvantages are due to the complexity of pooled measurements and a dearth of statistical tools. Herein, we developed a regression-based method to unzip pooled measurements, which facilitated a comprehensive assessment of disparities in exposure to these chemicals. We found increasing dependence of PCBs on age and income, whereas PBDEs were the highest among adolescents and seniors and were elevated among the low-income population. In addition, Hispanics had the lowest PCBs and PBDEs among all demographic groups after controlling for potential confounders. These findings can guide the development of population-specific interventions to promote environmental justice. Moreover, both chemical levels declined throughout the period, indicating the effectiveness of existing regulatory policies.

Time trends of PCDDs, PCDFs, DL-PCBs, and NDL-PCBs in Italian women from biomonitoring studies

A number of biomonitoring investigations were carried out in Italy between 2000 and 2018 by the Unit of Human Exposure to Environmental Chemicals of the Italian National Institute of Health (Rome). The investigations were characterized by different features (case-control or cross-sectional studies, participants from impacted or no impacted areas, etc), but despite the differences, it was possible to study the time trends of Σ₇(PCDDs), Σ₁₀(PCDFs), Σ₁₂(DL-PCBs), and Σ₆(NDL-PCBs) (analytical and TEQ cumulative concentrations, as appropriate). All the chemicals considered showed clear rates of a time-dependent concentration decrease, data having previously been adjusted for the sampled Region and subject age. Σ₇(PCDDs) exhibited the fastest decrease with an average rate of -4.44 pg/g-fat year^-1 (2009-2018), whereas Σ₆(NDL-PCBs) was characterized by the slowest decrease with a rate of -0.771 ng/g-fat year^-1 (2000-2018). A clear distinction between the decreasing rates of Σ₇(PCDDs) and Σ₁₀(PCDFs) was observed, as the latter decreased at half the rate of the Σ7(PCDDs). The slower rate of the Σ₁₀(PCDFs) decline may be due to an ongoing source of PCDFs in the environment beyond those traditionally considered for this group of contaminants such as the production of PCDFs due to PCBs thermal conversion from matrices contaminated with PCBs. Production of PCDFs due to thermal conversion of matrices contaminated with PCBs could be an ongoing source which may be of concern because recent data have highlighted the diffusion of PCBs in the European environment. The decreasing rates of PCDDs + PCDFs, DL-PCBs, and PCDDs + PCDFs + DL-PCBs - original analytical data converted to "dioxin equivalents" - were respectively estimated as (pgTEQ/g-fat year^-1) -2.08, -2.06, and -2.10, values exhibiting good compatibility between one another.

The Association between Blood Concentrations of PCDD/DFs, DL-PCBs and the Risk of Type 2 Diabetes Mellitus and Thyroid Cancer in South Korea

Background and Objectives: Epidemiological studies have inconsistently shown an association between dioxin and risk of type 2 diabetes mellitus (T2DM) and cancer. This study aims to examine the effects of blood concentration of dioxin-like polychlorinated biphenyls (DL-PCBs) and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/DFs) on T2DM and thyroid cancer. Methods: We conducted a nested case–control study within the Korean cancer prevention study-II (KCPS-II) consisting of 15 thyroid cancer cases, 30 T2DM cases, and 55 controls. A total of 500 samples were used in 100 pooling samples. An average value of a pooled sample was calculated weighted by the blood volume of each sample. Results: The study population included 100 participants from the KCPS-II (median (IQR) baseline age, 54.06 [21.04] years; 48 women). The toxic equivalents of PCDD/DFs showed a significant positive association with T2DM and thyroid cancer, after adjustments for potential confounders (T2DM ORs  =  1.23; 95% CI  =  1.05–1.43; thyroid cancer ORs  =  1.34; 95% CI  =  1.12–1.61). Conclusion: In this study, both T2DM and thyroid cancer were associated with the blood concentrations of PCDD/DFs. The association between PCDD/DFs and T2D was found among women but not among men. Our findings suggest that further biochemical in vivo research and epidemiologic studies are needed to clarify the association between dioxins concentrations and diseases.

High mortality and high PCDD/Fs exposure among residents downwind of municipal solid waste incinerators: A case study in China

Studies on the human body burden of dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in populations around municipal solid waste incinerators (MSWIs) in China are limited. The objective of this study was to assess the potential adverse health effects of an 8-year MSWI on the surrounding population and identify possible exposure pathways. We hypothesized that the MSWI would result in different environmental impacts and population health outcomes between upwind and downwind of its 3 km vicinity. We conducted a 10-year retrospective mortality survey on the population surrounding the MSWI. Then, we selected 50 residents aged 50 years or older on each of the upwind and downwind sides of MSWI to test serum PCDD/Fs. Meanwhile, environmental and food exposures to PCDD/Fs were tested for selected residents. The age-adjusted mortality rates were significantly higher for residents downwind than upwind, but no significant difference was found in the standardized mortality ratio before and after the MSWI operation. The toxic equivalents (TEQ) and major congeners of PCDD/Fs were significantly higher in the sera of the downwind residents than in the upwind. The PCDD/Fs in air, soil, dust, and vegetables on the downwind side were not significantly different from those on the upwind side, but the mean concentrations of PCDD/Fs in downwind hen eggs was significantly higher than those from upwind. In conclusion, downwind residents living within 3 km of the MSWI had higher age-adjusted mortality and serum level of PCDD/Fs than upwind residents. This higher mortality rate among downwind residents was not associated with MSWI. However, the higher levels of PCDD/Fs in downwind hen eggs suggest that the downwind population dioxin exposure was related to their location.

Decreasing temporal trends of polychlorinated dibenzo-p-dioxins and dibenzofurans in adipose tissue from residents near a hazardous waste incinerator

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are very toxic chemicals which are emitted in waste incineration and whose exposure has important adverse effects for the human health. In 2019, adipose tissue samples were collected from 15 individuals with a median age of 61 years, who had been living near a hazardous waste incinerator in Constantí (Spain). The content of PCDD/Fs in each sample was analyzed. The results were compared with data from previous studies, conducted before (1998) and after (2002, 2007 and 2013) the facility started to operate, and based on populations of similar age. In 2019, the mean concentration of PCDD/Fs in adipose tissue was 6.63 pg WHO-TEQ/g fat, ranging from 0.95 to 12.95 pg WHO-TEQ/g fat. A significant reduction was observed with respect to the baseline study (1998), when a mean PCDD/Fs concentration of 40.1 pg WHO-TEQ/g fat was found. Moreover, the current level was much lower than those observed in the 3 previous studies (9.89, 14.6 and 11.5 pg WHO-TEQ/g fat in 2002, 2007 and 2013, respectively). The body burdens of PCDD/Fs were strongly correlated with age. The significant reduction of PCDD/Fs levels in adipose tissue fully agreed with the decreasing trend of the dietary intake of PCDD/Fs by the population of the zone (from 210.1 pg I-TEQ/day in 2018 to 8.54 pg WHO-TEQ/day in 2018). Furthermore, a similar decrease has been also observed in other biological, such as breast milk and plasma. The current data in adipose tissue, as well as those in other biological monitors, indicate that the population living near the HWI is not particularly exposed to high levels of PCDD/Fs. However, biomonitoring studies cannot differentiate the impact of the HWI emissions from food consumption patterns. This question can be only solved by conducting complementary investigations and contrasting the results of monitoring and epidemiological studies.

Application of qualitative and quantitative uncertainty assessment tools in developing ranges of plausible toxicity values for 2,3,7,8-tetrachlorodibenzo-p-dioxin

Increasing interest in characterizing risk assessment uncertainty is highlighted by recent recommendations from the National Academy of Sciences. In this paper we demonstrate the utility of applying qualitative and quantitative methods for assessing uncertainty to enhance risk-based decision-making for 2,3,7,8-tetrachlorodibenzo-p-dioxin. The approach involved deconstructing the reference dose (RfD) via evaluation of the different assumptions, options, models and methods associated with derivation of the value, culminating in the development of a plausible range of potential values based on such areas of uncertainty. The results demonstrate that overall RfD uncertainty was high based on limitations in the process for selection (e.g., compliance with inclusion criteria related to internal validity of the co-critical studies, consistency with other studies), external validity (e.g., generalizing findings of acute, high-dose exposure scenarios to the general population), and selection and classification of the point of departure using data from the individual studies (e.g., lack of statistical and clinical significance). Building on sensitivity analyses conducted by the US Environmental Protection Agency in 2012, the resulting estimates of RfD values that account for the uncertainties ranged from ~1.5 to 179 pg/kg/day. It is anticipated that the range of RfDs presented herein, along with the characterization of uncertainties, will improve risk assessments of dioxins and provide important information to risk managers, because reliance on a single toxicity value limits the information needed for making decisions and gives a false sense of precision and accuracy.

TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs

Background

A significant number of adverse drug reactions is caused by unexpected Drug-drug interactions (DDIs). The identification of DDIs becomes crucial before the co-prescription of multiple drugs is made. Such a task in clinics or in drug discovery usually requires high costs and numerous limitations, while computational approaches are able to predict potential DDIs effectively by utilizing diverse drug attributes (e.g. side effects). Nevertheless, they’re incapable when required to predict enhancive and degressive DDIs, which change increasingly and decreasingly the pharmacological behavior of interacting drugs respectively. The pharmacological change of DDIs is one of the most important factors when making a multi-drug prescription.

Results

In this work, we design a Triple Matrix Factorization-based Unified Framework (TMFUF) to address the above issue. By leveraging a group of side effect entries of drugs, TMFUF achieves the inspiring result (AUC = 0.842 and AUPR = 0.526) in the case of conventional DDI prediction under the traditional screening task. In the comparison with two state-of-the-art approaches, TMFUF demonstrates it superiority by ~ 7% and ~ 20% improvement in terms of AUC and AUPR respectively. More importantly, TMFUF shows its ability in the comprehensive DDI prediction under different screening tasks. Finally, a utilization TMFUF reveals the significant pairs of side effects, which contribute to form enhancive and degressive DDIs, for further clinical validation.

Conclusions

The proposed TMFUF is first capable to predict both conventional binary DDIs and comprehensive DDIs such that it captures the pharmacological changes caused by DDIs. Furthermore, it provides a unified solution of DDI prediction for two screening scenarios, which involves newly given drugs having no prior interaction. Another advantage is its ability to indicate how significantly the pairs of drug features contribute to form DDIs.

Exposure of dioxin-like chemicals in participants of the Anniston community health survey follow-up

The 2014 follow-up of the Anniston Community Health Survey (ACHS II) consisted of 338 surviving participants from the 2005-2007 baseline study (ACHS) who had previous polychlorinated biphenyl (PCB) measurements, were not pregnant, and were not institutionalized. Questionnaires and blood samples provided the demographic, personal history, and chemical concentration data of the Anniston residents. Approximately 51% of participants were African American, 72% were female, and the mean age was 63 years old. The objectives of this study were to provide an exposure assessment of dioxin-like chemicals in the ACHS II participants and compare the measurements with the general United States (U.S.) population via the National Health and Nutrition Examination Survey (NHANES). Stratified analyses revealed significantly higher average total dioxin toxic equivalencies (TEQs) among African Americans compared to Whites (33.1 vs. 19.2 pg/g lipid), and in females compared to males (29.8 vs. 17.0 pg/g lipid). When adjusting for age, sex, and race in linear regression, we found ACHS II participants to have significantly higher total dioxin TEQ than the general 2014 U.S. population that we estimated for using half-life and NHANES 2003/04 data (most recent NHANES individual samples data), by 16.7 pg/g lipid. Principal component analyses showed that non-ortho and mono-ortho PCBs were separated from the other dioxin-like chemicals among the Anniston residents, whereas the chemicals were all clustered together for estimated NHANES 2014. The concentrations of dioxin-like chemicals, especially non-ortho and mono-ortho PCBs, in Anniston residents who resided near the former PCB production plant were higher than those in the general U.S.

POPULATION

Although data strongly supported this difference, these inferences are limited because NHANES 2013/14 data were unavailable and we used estimated NHANES 2014 levels that we imputed from NHANES 2003/04 data in conjunction with half-life values estimated from Milbrath et al., 2009.

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization

Background

Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription.

Results

In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs.

Conclusions

Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree and negative degree; (3) though the binary DDI network contains no information about enhancive and degressive DDIs at all, it implies some of their relationship in the comprehensive DDI matrix; (4) the occurrence of signs indicating enhancive and degressive DDIs is not random because the comprehensive DDI network is equipped with a structural balance.