Nonmonotonous metabolic response of mammalian cells and tissues to ionizing radiation

Changes in the activity of ornithindecarboxylase in various tissues and in the amount of catecholamine in rat hypothalamus by the action of acute and chronic ionizing radiation were studied. A nonmonotonous relationship between the metabolic parameters of animal tissues and cells and the radiation dose was revealed. It was assumed that the nonmonotonous character of the dose-response dependence results from the nonmonotonous time course of the metabolic response to irradiation. It was also assumed that living systems have the property of responding to stress agents by nonmonotonous changes in metabolism. In the case of acute irradiation, this response manifests itself as oscillations of metabolic parameters about the control. The oscillations occur with a particular amplitude and periods, which vary with radiation dose, and damp out with time. As a result, in a fixed time interval, the dose-response curve may be nonmonotonous. Reverse dose-response relationships are also possible. In the case of chronic irradiation, the metabolic and functional parameters oscillate throughout irradiation time, and a modification of the response occurs. A prolong exposure to ionizing radiation causes strong changes in the metabolism of lipids of cell membranes, organelles and chromatin, as well as in the functional properties of some mammalian cells and tissues. The necessity of constructing quantitative models for explaining the nonmonotonous dose-response dependence is discussed.     

Dose-response and threshold-mediated mechanisms in mutagenesis: statistical models and study design

The objective of this paper is to review the use, in mutagenesis, of various mathematical models to describe the dose-response relationship and to try to identify thresholds. It is often taken as axiomatic that genotoxic carcinogens could damage DNA at any level of exposure, leading to a mutation, and that this could ultimately result in tumour development. This has led to the assumption that for genotoxic chemicals, there is no discernible threshold. This assumption is increasingly being challenged in the case of aneugens. The distinction between 'absolute' and 'pragmatic' thresholds is made and the difficulties in determining 'absolute' thresholds using hypothesis testing approaches are described. The potential of approaches, based upon estimation rather than statistical significance for the characterization of dose-response relationships, is stressed. The achievement of a good fit of a mathematical model to experimental data is not proof that the mechanism supposedly underlying this model is operating. It has been argued, in the case of genotoxic chemicals, that any effects produced by a genotoxic chemical which augments that producing a background incidence in unexposed individuals will lead to a dose-response relationship that is non-thresholded and is linear at low doses. The assumptions underlying this presumption are explored in the context of the increasing knowledge of the mechanistic basis of mutagenicity and carcinogenicity. The possibility that exposure to low levels of genotoxic chemicals may induce and enhance defence and repair mechanisms is not easily incorporated into many of the existing mathematical models and should be an objective in the development of the next generation of biologically based dose-response (BB-DR) models. Studies aimed at detecting or characterizing non-linearities in the dose-response relationship need appropriate experimental designs with careful attention to the choice of biomarker, number and selection of dose levels, optimum allocation of experimental units and appropriate levels of replication within and repetition of experiments. The characterization of dose-response relationships with appropriate measures of uncertainty can help to identify 'pragmatic' thresholds based upon biologically relevant criteria which can help in the regulatory process.     

Aspirin exposure during the first 20 weeks of gestation and IQ at four years of age

The relationship between maternal aspirin use during the first 20 weeks of pregnancy and the child's IQ at 4 years of age was investigated in 19,226 pregnancies occurring from 1959 to 1966 in the Collaborative Perinatal Project. The mean IQ of children exposed to aspirin was 98.3, which was 2.1 points higher (95% confidence interval = 1.7, 2.6; P less than 0.0001) than that of unexposed children. Adjustment for multiple social, demographic, and other confounders reduced this difference to less than one point in favor of the aspirin exposed group, although statistical significance remained. Total days of exposure was used as an index of dose, and no dose-response relationship between aspirin use and IQ was found. The effect of prenatal aspirin exposure did not vary by infant sex. It is concluded that an adverse effect of aspirin exposure on IQ is unlikely.     

Dose-response relationship for parathyroid adenoma after exposure to ionizing radiation in infancy

Several authors have suggested that there is an excess risk of hyperparathyroidism, adenomas or hyperplasia after exposure to ionizing radiation. There is still, however, some uncertainty about this association, because these diseases are often asymptomatic and escape clinical detection if not specially searched for. This study is based on a pooled Swedish cohort of 27,925 persons with skin hemangiomas. The majority received radiation treatment in infancy between 1920 and 1965 in Stockholm and Gothenburg. The mean age at treatment was 6 months and the median thyroid dose was 0.20 Gy (range 0-28.5 Gy). Record linkage with the Swedish Cancer Register for the period 1958-1997 gave 43 cases of parathyroid adenoma in the cohort. Analyses of excess relative risk (ERR) models were performed using Poisson regression methods. Clinical records were scrutinized to determine if the childhood radiation exposure was known (biased cases) at the time of diagnosis. Seven of the cases of parathyroid adenoma were classified as biased cases. The standardized incidence ratio (SIR) was 2.10 (95% confidence interval 1.52-2.82) when all cases were included and 1.76 (95% CI 1.23-2.43) with the biased cases excluded. A linear dose-response model with stratification for sex fitted the data best. The ERR per gray was 3.84 (95% CI 1.56-8.99) with all cases and 1.56 (95% CI 0.36-4.45) with the biased cases excluded. There was a significant difference in the ERR per gray between the two subcohorts, probably because of different diagnostic activity in the regions. Our findings confirm that there is a dose-response relationship for radiation-induced parathyroid adenomas.     

Dose response problems in carcinogenesis.

The estimation of risks from exposure to carcinogens is an important problem from the viewpoint of protection of human health. It also poses some very difficult dose-response problems. Two dose-response models may fit experimental data about equally well and yet predict responses that differ by many orders of magnitude at low doses. Mechanisms of carcinogenesis are not sufficiently understood so that the shape of the dose-response curve at low doses can be satisfactorily predicted. Mathematical theories of carcinogenesis and statistical procedures can be of use with dose-reponse problems such as this and, in addition, can lead to a better understanding of the mechanisms of carcinogenesis. In this paper, mathematical dose-response models of carcinogenesis are considered as well as various proposed dose-response procedures for estimating carcinogenic risks at low doses. Areas are suggested in which further work may be useful. These areas include experimental design problems, statistical procedures for use with time-to-occurrence data, and mathematical models that incorporate such biological features as pharmacokinetics of carcinogens, synergistic effects, DNA repair, susceptible subpopulations, and immune reactions.     

Bayesian semiparametric analysis of developmental toxicology data

Modeling of developmental toxicity studies often requires simple parametric analyses of the dose-response relationship between exposure and probability of a birth defect but poses challenges because of nonstandard distributions of birth defects for a fixed level of exposure. This article is motivated by two such experiments in which the distribution of the outcome variable is challenging to both the standard logistic model with binomial response and its parametric multistage elaborations. We approach our analysis using a Bayesian semiparametric model that we tailored specifically to developmental toxicology studies. It combines parametric dose-response relationships with a flexible nonparametric specification of the distribution of the response, obtained via a product of Dirichlet process mixtures approach (PDPM). Our formulation achieves three goals: (1) the distribution of the response is modeled in a general way, (2) the degree to which the distribution of the response adapts nonparametrically to the observations is driven by the data, and (3) the marginal posterior distribution of the parameters of interest is available in closed form. The logistic regression model, as well as many of its extensions such as the beta-binomial model and finite mixture models, are special cases. In the context of the two motivating examples and a simulated example, we provide model comparisons, illustrate overdispersion diagnostics that can assist model specification, show how to derive posterior distributions of the effective dose parameters and predictive distributions of response, and discuss the sensitivity of the results to the choice of the prior distribution.     

Is Antiretroviral Therapy Causing Long-Term Liver Damage? A Comparative Analysis of HIV-Mono-Infected and HIV/Hepatitis C Co-Infected Cohorts

The effects of highly active antiretroviral therapy (HAART) on progression of hepatic fibrosis in HIV-hepatitis C virus (HCV) co-infection are not well understood. Deaths from liver diseases have risen in the post-HAART era, yet some cross-sectional studies have suggested that HAART use is associated with improved fibrosis rates. In a retrospective cohort of 533 HIV mono-infected and 127 HIV/HCV co-infected patients, followed between January 1991 and July 2005 at a university-based HIV clinic, we investigated the relationship between cumulative HAART exposure and hepatic fibrosis, as measured by the aspartate aminotransferase-to-platelet ratio index (APRI). We used a novel methodological approach to estimate the dose-response relationship of the effect of HAART exposure on APRI. HAART was associated with increasing APRI over time in HIV/HCV co-infected patients suggesting that they may be experiencing cumulative hepatotoxicity from antiretrovirals. The estimated median change (95% confidence interval) in APRI per one year of HAART intake was of −0.46% (−1.61% to 0.71%) in HIV mono-infected compared to 2.54% (−1.77% to 7.03%) in HIV/HCV co-infected patients. Similar results were found when the direct effect of HAART intake since the last visit was estimated on the change in APRI. HAART use associated is with increased APRI in patients with HIV/HCV co-infection. Therefore treatment for HCV infection may be required to slow the growing epidemic of end-stage liver disease in this population.     

Prevalence of anti-hepatitis C virus antibody and chronic liver disease among atomic bomb survivors

To investigate whether exposure to atomic bomb radiation altered the prevalence of hepatitis C virus (HCV) infection or accelerated the progress toward chronic hepatitis after HCV infection, the seropositivity of antibody to hepatitis C virus (anti-HCV) was determined for 6,121 participants in the Adult Health Study of atomic bomb survivors in Hiroshima and Nagasaki. The seropositivity of anti-HCV antibody was 2.5 times higher among those with a history of blood transfusion and 1.2 times higher among those with a family history of liver disease, whereas acupuncture showed no association with anti-HCV. Although the prevalence of anti-HCV was lower for survivors with positive dose estimates than for those with 0 dose (relative prevalence 0.84, P = 0.022), there was no evidence of a smooth dose-response relationship. However, these data suggested that the radiation dose response for chronic liver disease among HCV antibody-positive survivors may be greater than that among HCV antibody-negative survivors (slope ratio 20). In conclusion, no dose-response relationship was found between anti-HCV positivity and radiation dose; a possible increase in the radiation dose response of chronic liver disease among anti-HCV-positive individuals was found. Thus radiation exposure may accelerate the progress of chronic liver disease associated with hepatitis C virus infection.     

Mortality from cardiovascular diseases in the Semipalatinsk historical cohort, 1960-1999, and its relationship to radiation exposure

The data on risk of mortality from cardiovascular disease due to radiation exposure at low or medium doses are inconsistent. This paper reports an analysis of the Semipalatinsk historical cohort exposed to radioactive fallout from nuclear testing in the vicinity of the Semipalatinsk Nuclear Test Site, Kazakhstan. The cohort study, which includes 19,545 persons of exposed and comparison villages in the Semipalatinsk region, had been set up in the 1960s and comprises 582,656 person-years of follow-up between 1960 and 1999. A dosimetric approach developed by the U.S. National Cancer Institute (NCI) has been used. Radiation dose estimates in this cohort range from 0 to 630 mGy (whole-body external). Overall, the exposed population showed a high mortality from cardiovascular disease. Rates of mortality from cardiovascular disease in the exposed group substantially exceeded those of the comparison group. Dose-response analyses were conducted for both the entire cohort and the exposed group only. A dose-response relationship that was found when analyzing the entire cohort could be explained completely by differences between the baseline rates in exposed and unexposed groups. When taking this difference into account, no statistically significant dose-response relationship for all cardiovascular disease, for heart disease, or for stroke was found. Our results suggest that within this population and at the level of doses estimated, there is no detectable risk of radiation-related mortality from cardiovascular disease.     

Improving Risk Assessment: Toxicological Research Needs

A workshop convened to define research needs in toxicology identified several deficiencies in data and methods currently applied in risk assessment. The workshop panel noted that improving the link between chemical exposure and toxicological response requires a better understanding of the biological basis for inter-and intra-human variability and susceptibility. This understanding will not be complete unless all life stages are taken into consideration. Because animal studies serve as a foundation for toxicological assessment, proper accounting for cross-species extrapolation is essential. To achieve this, adjustments for dose-rate effects must be improved, which will aid in extrapolating toxicological responses to low doses and from short-term exposures. Success depends on greater use of validated biologically based dose-response models that include pharmacokinetic and pharmacodynamic data. Research in these areas will help define uncertainty factors and reduce reliance on underlying default assumptions. Throughout the workshop the panel recognized that biomedical science and toxicology in particular is on the verge of a revolution because of advances in genomics and proteomics. Data from these high-output technologies are anticipated to greatly improve risk assessment by enabling scientists to better define and model the elements of the relationship between exposure to biological hazards and health risks in populations with differing susceptibilities.     

A dose-response model for skin cancer induction by chronic u.v. exposure of a human population

A dose-response model, based on the results of animal experiments, is presented for skin cancer induction in a human population by chronic exposure to ultraviolet radiation. The model takes into account a variety of exposure habits and susceptibilities of the individuals in the population. The required input data for the dose-response relationship are the age specific incidences of the population in question.Calculations based on this model can be used as a step in the evaluation of the effect which a reduction of stratospheric ozone would have on the non-melanoma skin cancer incidence. As an example an evaluation for the white population of the U.S.A. is presented. The estimate resulting from this evaluation agrees fairly well with earlier estimates based on combined climatological and epidemiological data.     

Maximum likelihood estimation for cytogenetic dose-response curves

In vitro dose-response curves are used to describe the relation between chromosome aberrations and radiation dose for human lymphocytes. The lymphocytes are exposed to low-LET radiation, and the resulting dicentric chromosome aberrations follow the Poisson distribution. The expected yield depends on both the magnitude and the temporal distribution of the dose. A general dose-response model that describes this relation has been presented by Kellerer and Rossi (1972, Current Topics on Radiation Research Quarterly 8, 85-158; 1978, Radiation Research 75, 471-488) using the theory of dual radiation action. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting dose-time-response models are intrinsically nonlinear in the parameters. A general-purpose maximum likelihood estimation procedure is described, and estimation for the nonlinear models is illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure.     

Tumor induction in BALB/c female mice after fission neutron or gamma irradiation

This study was designed to examine the dose-response relationships for tumor induction after neutron irradiation in female BALB/c mice, with emphasis on the response in the dose range 0 to 50 rad. Tumors induced after radiation exposure included ovarian tumors, lung adenocarcinomas, and mammary adenocarcinomas. For comparison the dose responses for induction of these tumors after 137Cs gamma irradiation were also examined. As previously described for the female RFM mouse, the data for ovarian tumor induction after neutron and gamma irradiation were consistent with a threshold model. For lung and mammary tumors the dose-response curve after neutron irradiation appeared to "bend over" in the dose range 10 to 20 rad. The factors responsible for this bend-over and their relative contributions to the overall form of the dose-response relationship are not presently known. However, these data strongly indicate that extrapolation from data above 50 rad could result in a significant underestimate of risks. Further, it is clear that current models of neutron carcinogenesis are inadequate, since such a bend-over is not predicted at these low dose levels.     

Shared uncertainty in measurement error problems, with application to Nevada Test Site fallout data

In radiation epidemiology, it is often necessary to use mathematical models in the absence of direct measurements of individual doses. When complex models are used as surrogates for direct measurements to estimate individual doses that occurred almost 50 years ago, dose estimates will be associated with considerable error, this error being a mixture of (a) classical measurement error due to individual data such as diet histories and (b) Berkson measurement error associated with various aspects of the dosimetry system. In the Nevada Test Site(NTS) Thyroid Disease Study, the Berkson measurement errors are correlated within strata. This article concerns the development of statistical methods for inference about risk of radiation dose on thyroid disease, methods that account for the complex error structure inherence in the problem. Bayesian methods using Markov chain Monte Carlo and Monte-Carlo expectation-maximization methods are described, with both sharing a key Metropolis-Hastings step. Regression calibration is also considered, but we show that regression calibration does not use the correlation structure of the Berkson errors. Our methods are applied to the NTS Study, where we find a strong dose-response relationship between dose and thyroiditis. We conclude that full consideration of mixtures of Berkson and classical uncertainties in reconstructed individual doses are important for quantifying the dose response and its credibility/confidence interval. Using regression calibration and expectation values for individual doses can lead to a substantial underestimation of the excess relative risk per gray and its 95% confidence intervals.     

Combining multiple comparisons and modeling techniques in dose-response studies

The analysis of data from dose-response studies has long been divided according to two major strategies: multiple comparison procedures and model-based approaches. Model-based approaches assume a functional relationship between the response and the dose, taken as a quantitative factor, according to a prespecified parametric model. The fitted model is then used to estimate an adequate dose to achieve a desired response but the validity of its conclusions will highly depend on the correct choice of the a priori unknown dose-response model. Multiple comparison procedures regard the dose as a qualitative factor and make very few, if any, assumptions about the underlying dose-response model. The primary goal is often to identify the minimum effective dose that is statistically significant and produces a relevant biological effect. One approach is to evaluate the significance of contrasts between different dose levels, while preserving the family-wise error rate. Such procedures are relatively robust but inference is confined to the selection of the target dose among the dose levels under investigation. We describe a unified strategy to the analysis of data from dose-response studies which combines multiple comparison and modeling techniques. We assume the existence of several candidate parametric models and use multiple comparison techniques to choose the one most likely to represent the true underlying dose-response curve, while preserving the family-wise error rate. The selected model is then used to provide inference on adequate doses.     

Quantification of the Effects of Age on the Dose Response of Variola major in Suckling Mice

Dose-response data for Variola major (V. major), the causative agent of smallpox, were obtained from the open literature, summarized, and fitted with three dose-response models. It is known from prior outbreak experience that there is generally a difference in infectivity of the agent and its subsequent mortality depending on the age of the patient. A source of animal dose-response data were found with age delineation for the exposure group (suckling mice, intraperitoneal exposure). This delineation was used to adapt current dose-response models to include an age dependency parameter. The degree of the models' fit to the data was ascertained using maximum likelihood estimation (MLE). The effect of host age could be described quantitatively using modifications to the beta Poisson and exponential dose-response models. The modifications improvement in the accuracy of risk prediction by 72% for the beta Poisson model and 7% for the exponential model, compared to the original (unmodified) models.     

Association between shift work and risk of type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of observational studies

ABSTRACT

To evaluate the association between shift work and risk of type 2 diabetes mellitus, we searched PubMed, EMBASE and Web of Science from their inception to June 8, 2019. Observational studies examining the relationship between shift work and type 2 diabetes were included. Subgroup analyses were conducted to explore whether specific characteristics would affect the relationship. A dose-response relationship was estimated by using generalized least squares trend regression. Finally, twelve cohort studies and nine cross-sectional studies were included (inter-rater agreement, k = 0.96). The result of meta-analysis indicated that shift work was associated with an increased risk of type 2 diabetes (relative risk = 1.10, 95% confidence interval = 1.05–1.14). Subgroup analyses demonstrated that female shift workers have increased risk of type 2 diabetes while male not observed, health care workers showed the highest risk compared with civil servants and manual workers, and night shift and rotating shift were associated with an increased risk of type 2 diabetes. Dose-response meta-analysis based on three cohorts among female workers indicated that there might be a positive association between duration of shift work and the risk of type 2 diabetes. In conclusion, shift work is positively associated with an increased risk of type 2 diabetes. Among female workers, with the years of exposure to shift work prolonged, the risk of type 2 diabetes might increase accordingly. In the future, more studies are needed to confirm the results of dose-response analysis.     

A systems-based mathematical modelling framework for investigating the effect of drugs on solid tumours

Background and Purpose

Most information on the dose-response of radiation-induced cancer is derived from data on the A-bomb survivors. Since, for radiation protection purposes, the dose span of main interest is between zero and one Gy, the analysis of the A-bomb survivors is usually focused on this range. However, estimates of cancer risk for doses larger than one Gy are becoming more important for radiotherapy patients. Therefore in this work, emphasis is placed on doses relevant for radiotherapy with respect to radiation induced solid cancer.

Materials and methods

For various organs and tissues the analysis of cancer induction was extended by an attempted combination of the linear-no-threshold model from the A-bomb survivors in the low dose range and the cancer risk data of patients receiving radiotherapy for Hodgkin's disease in the high dose range. The data were fitted using organ equivalent dose (OED) calculated for a group of different dose-response models including a linear model, a model including fractionation, a bell-shaped model and a plateau-dose-response relationship.

Results

The quality of the applied fits shows that the linear model fits best colon, cervix and skin. All other organs are best fitted by the model including fractionation indicating that the repopulation/repair ability of tissue is neither 0 nor 100% but somewhere in between. Bone and soft tissue sarcoma were fitted well by all the models. In the low dose range beyond 1 Gy sarcoma risk is negligible. For increasing dose, sarcoma risk increases rapidly and reaches a plateau at around 30 Gy.

Conclusions

In this work OED for various organs was calculated for a linear, a bell-shaped, a plateau and a mixture between a bell-shaped and plateau dose-response relationship for typical treatment plans of Hodgkin's disease patients. The model parameters (α and R) were obtained by a fit of the dose-response relationships to these OED data and to the A-bomb survivors. For any three-dimensional inhomogenous dose distribution, cancer risk can be compared by computing OED using the coefficients obtained in this work.