The role of reactive oxygen species and oxidative stress in environmental carcinogenesis and biomarker development

Although we have greatly benefited from the use of traditional epidemiological approaches in linking environmental exposure to human disease, we are still lacking knowledge in to how such exposure participates in disease development. However, molecular epidemiological studies have provided us with evidence linking oxidative stress with the pathogenesis of human disease and in particular carcinogenesis. To this end, oxidative stress-based biomarkers have proved to be essential in revealing how oxidative stress may be mediating toxicity induced by many known carcinogenic environmental agents. Therefore, throughout this review article, we aim to address the current state of oxidative stress-based biomarker development with major emphasis pertaining to biomarkers of DNA, lipid and protein oxidation.     

Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data

Summary .   In clinical studies, longitudinal biomarkers are often used to monitor disease progression and failure time. Joint modeling of longitudinal and survival data has certain advantages and has emerged as an effective way to mutually enhance information. Typically, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out to be more elusive than models for standard longitudinal studies in which no survival endpoint occurs. In this article, we propose a nonparametric multiplicative random effects model for the longitudinal process, which has many applications and leads to a flexible yet parsimonious nonparametric random effects model. A proportional hazards model is then used to link the biomarkers and event time. We use B-splines to represent the nonparametric longitudinal process, and select the number of knots and degrees based on a version of the Akaike information criterion (AIC). Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Due to the simplicity of the model structure, the proposed approach has good numerical stability and compares well with the competing parametric longitudinal approaches. The new approach is illustrated with primary biliary cirrhosis (PBC) data, aiming to capture nonlinear patterns of serum bilirubin time courses and their relationship with survival time of PBC patients.     

Combining environmental exposure and genetic effect measurements in health outcome assessment.

The presence of overwhelming difficulties in assessing the extent or even the presence of a causal association between modern environmental exposures and disease has promoted the use of more complex models in the design of human biomonitoring studies. The concatenation of environmental exposure, genetic effect and individual susceptibility is a key issue in the assessment of risks for populations exposed to environmental pollutants. The use of a biological event laying in the causal pathway from exposure to outcome as surrogate end-point of disease, can potentially anticipate clinical diagnosis, offering a number of possibilities for application of preventive measures. Numerous biomarkers are currently employed to study human populations exposed to environmental carcinogens, among these, the frequency of chromosomal aberration (CA) in peripheral blood lymphocytes has the most abundant literature linking a genetic effect with the occurrence of cancer. Findings from recent epidemiological studies which have followed-up a large group of healthy subjects screened for CAs have lent further support to the use of chromosomal breakage as a relevant biomarker of cancer risk. The applicability of surrogate end-points of cancer on an individual basis thus far seems to be limited to few examples. On the other hand, from a public health outlook, increases in the frequency of surrogate end-points are suggestive of an increased risk of cancer, and for validated biomarkers such as CAs intervention policies and actions in exposed populations showing increased frequency of these end-points should be always recommended.     

Design issues in cross-sectional biomarkers studies: urinary biomarkers of PAH exposure and oxidative stress

Cross-sectional biomarker studies can provide a snapshot of the frequency and characteristics of exposure/disease in a population at a particular point in time and, as a result, valuable insights for delineating the multi-step association between exposure and disease occurrence. Three major issues should be considered when designing biomarker studies: selection of appropriate biomarkers, the assay (laboratory validity), and the population validity of the selected biomarkers. Factors related to biomarker selection include biological relevance, specificity, sensitivity, biological half-life, stability, and so on. The assay attributes include limit of detection, reproducibility/reliability, inter-laboratory variation, specificity, time, and cost. Factors related to the population validity include the frequency or prevalence of markers, greater inter-individual variation than intra-individual variation, intra-class correlation coefficients (ICC), association with potential confounders, invasiveness of specimen collection, and subject selection. Three studies are selected to demonstrate different features of cross-sectional biomarker studies: (1) characterizing the determinants of the biomarkers (study I: urinary PAH metabolites and environmental particulate exposure), (2) relationship of multiple biomarkers of exposure and effect (study II: relationship between urinary PAH metabolites and oxidative stress), and (3) evaluating gene-environmental interaction (study III: effect of genetic polymorphisms of GSTM1 on the association of green tea consumption and urinary 1-OHPG levels in shipbuilding workers).     

A Hybrid EM and Monte Carlo EM Algorithm and Its Application to Analysis of Transmission of Infectious Diseases

Summary In epidemics of infectious diseases such as influenza, an individual may have one of four possible final states: prior immune, escaped from infection, infected with symptoms, and infected asymptomatically. The exact state is often not observed. In addition, the unobserved transmission times of asymptomatic infections further complicate analysis. Under the assumption of missing at random, data‐augmentation techniques can be used to integrate out such uncertainties. We adapt an importance‐sampling‐based Monte Carlo Expectation‐Maximization (MCEM) algorithm to the setting of an infectious disease transmitted in close contact groups. Assuming the independence between close contact groups, we propose a hybrid EM‐MCEM algorithm that applies the MCEM or the traditional EM algorithms to each close contact group depending on the dimension of missing data in that group, and discuss the variance estimation for this practice. In addition, we propose a bootstrap approach to assess the total Monte Carlo error and factor that error into the variance estimation. The proposed methods are evaluated using simulation studies. We use the hybrid EM‐MCEM algorithm to analyze two influenza epidemics in the late 1970s to assess the effects of age and preseason antibody levels on the transmissibility and pathogenicity of the viruses.     

Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.     

MicroRNAs as biomarkers of harmful environmental and occupational exposures: a systematic review

Abstract

Environmental exposure is a growing public health burden associated with several negative health effects. An estimated 4.2 million deaths occur each year from ambient air pollution alone. Biomarkers that reflect specific exposures have the potential to measure the real integrated internal dose from all routes of complex environmental exposure. MicroRNAs (miRNAs), small non-coding RNAs that regulate gene expression, have been studied as biomarkers in various diseases and have also shown potential as environmental exposure biomarkers. Here, we review the available human epidemiological and experimental evidence of miRNA expression changes in response to specific environmental exposures including airborne particulate matter. In doing so, we establish that miRNA exposure biomarker development remains in its infancy and future studies will need to carefully consider biological and analytical ‘design rules’ in order to facilitate clinical translation.     

Using multiple biomarkers and determinants to obtain a better measurement of oxidative stress: a latent variable structural equation model approach

Purpose: Since oxidative stress involves a variety of cellular changes, no single biomarker can serve as a complete measure of this complex biological process. The analytic technique of structural equation modeling (SEM) provides a possible solution to this problem by modelling a latent (unobserved) variable constructed from the covariance of multiple biomarkers.

Methods: Using three pooled datasets, we modelled a latent oxidative stress variable from five biomarkers related to oxidative stress: F₂-isoprostanes (FIP), fluorescent oxidation products, mitochondrial DNA copy number, γ-tocopherol (Gtoc) and C-reactive protein (CRP, an inflammation marker closely linked to oxidative stress). We validated the latent variable by assessing its relation to pro- and anti-oxidant exposures.

Results: FIP, Gtoc and CRP characterized the latent oxidative stress variable. Obesity, smoking, aspirin use and β-carotene were statistically significantly associated with oxidative stress in the theorized directions; the same exposures were weakly and inconsistently associated with the individual biomarkers.

Conclusions: Our results suggest that using SEM with latent variables decreases the biomarker-specific variability, and may produce a better measure of oxidative stress than do single variables. This methodology can be applied to similar areas of research in which a single biomarker is not sufficient to fully describe a complex biological phenomenon.     

Influence of systems biology response and environmental exposure level on between-subject variability in breath and blood biomarkers

To explain the underlying causes of apparently stochastic disease, current research is focusing on systems biology approaches wherein individual genetic makeup and specific ‘gene–environment’ interactions are considered. This is an extraordinarily complex task because both the environmental exposure profiles and the specific genetic susceptibilities presumably have large variance components. In this article, the focus is on the initial steps along the path to disease outcome namely environmental uptake, biologically available dose, and preclinical effect. The general approach is to articulate a conceptual model and identify biomarker measurements that could populate the model with hard data. Between-subject variance components from different exposure studies are used to estimate the source and magnitude of the variability of biomarker measurements. The intent is to determine the relative effects of different biological media (breath or blood), environmental compounds and their metabolites, different concentration levels, and levels of environmental exposure control. Examples are drawn from three distinct exposure biomarker studies performed by the US Environmental Protection Agency that studied aliphatic and aromatic hydrocarbons, trichloroethylene and methyl tertiary butyl ether. All results are based on empirical biomarker measurements of breath and blood from human subjects; biological specimens were collected under appropriate Institutional Review Board protocols with informed consent of the subjects. The ultimate goal of this work is to develop a framework for eventually assessing the total susceptibility ranges along the toxicological pathway from exposure to effect. The investigation showed that exposures are a greater contributor to biomarker variance than are internal biological parameters.     

生物标志物及其在生态系统研究中的应用

生物标志物是环境和地质体中记载着原始生物母质分子结构信息的有机化合物, 其含量可以指征特定生物来源对天然有机质的相对贡献, 其组成和同位素信息还可以记录有机质的转化及环境信息。与传统元素及组分分析相比, 生物标志物为研究天然有机质的来源、动态变化和转化特征提供了具有高度专一性和灵敏度的工具, 因此, 近年来被广泛地应用于生态学和生物地球化学研究中。特别是, 与生态系统观测以及控制实验相结合, 生物标志物在揭示微生物的活性与碳源变化、土壤有机碳的稳定机制及其对全球变化的响应等方面显示了广阔的应用前景。近些年开发的生物标志物单体同位素分析也在生态系统碳氮周转与食物网研究等方面显示了巨大的研究潜力。基于此, 该文综述了生态系统研究中常用的生物标志物的种类、分析方法和应用方向, 总结了生物标志物研究目前存在的问题, 并对未来的研究方向进行了展望, 旨在为使用生物标志物的生态学和环境科学研究者提供参考。     

Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure

Summary.   The present article deals with informative missing (IM) exposure data in matched case–control studies. When the missingness mechanism depends on the unobserved exposure values, modeling the missing data mechanism is inevitable. Therefore, a full likelihood-based approach for handling IM data has been proposed by positing a model for selection probability, and a parametric model for the partially missing exposure variable among the control population along with a disease risk model. We develop an EM algorithm to estimate the model parameters. Three special cases: (a) binary exposure variable, (b) normally distributed exposure variable, and (c) lognormally distributed exposure variable are discussed in detail. The method is illustrated by analyzing a real matched case–control data with missing exposure variable. The performance of the proposed method is evaluated through simulation studies, and the robustness of the proposed method for violation of different types of model assumptions has been considered.     

基于生物标志物指数法的海洋环境评价方法综述

生物标志物对化学污染物具有“早期预警”功能,在海洋环境评价中应用广泛.以生物标志物为基础的综合指数能够整合多种标志物对环境状况的响应,因而成为评价环境质量的有用工具.这些综合指数方法包括多生物标志物污染指数(MPI)、综合生物标志物响应指数(IBR)、生物效应评价指数(BAI)、生物标志物响应指数(BRI)、健康状态指数(HIS)等.本文从生物标志物指标体系确定、综合指数计算方法、污染程度分级、应用效果等方面对这些评价方法进行综述,并对基于生物标志物指数的海洋环境评价方法存在的问题和改进建议进行了探讨.     

Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.     

Estimating covariate‐adjusted measures of diagnostic accuracy based on pooled biomarker assessments

There is a need for epidemiological and medical researchers to identify new biomarkers (biological markers) that are useful in determining exposure levels and/or for the purposes of disease detection. Often this process is stunted by high testing costs associated with evaluating new biomarkers. Traditionally, biomarker assessments are individually tested within a target population. Pooling has been proposed to help alleviate the testing costs, where pools are formed by combining several individual specimens. Methods for using pooled biomarker assessments to estimate discriminatory ability have been developed. However, all these procedures have failed to acknowledge confounding factors. In this paper, we propose a regression methodology based on pooled biomarker measurements that allow the assessment of the discriminatory ability of a biomarker of interest. In particular, we develop covariate‐adjusted estimators of the receiver‐operating characteristic curve, the area under the curve, and Youden's index. We establish the asymptotic properties of these estimators and develop inferential techniques that allow one to assess whether a biomarker is a good discriminator between cases and controls, while controlling for confounders. The finite sample performance of the proposed methodology is illustrated through simulation. We apply our methods to analyze myocardial infarction (MI) data, with the goal of determining whether the pro‐inflammatory cytokine interleukin‐6 is a good predictor of MI after controlling for the subjects' cholesterol levels.     

Biomarkers in molecular epidemiology studies for health risk prediction

The field of molecular epidemiology is very promising, as sophisticated techniques are being developed to address etiology, genetic susceptibility and mechanisms for induction of disease. The use of biomarkers plays a key role in these investigations because the information can be used to predict the development of disease and to implement disease prevention programs. However, as emphasized by Frederica P. Perera, the field is strewn with studies either that failed to use validated biomarkers or whose designs did not adequately consider the biology of the endpoints, and the availability of validated biomarkers of health risk is still limited. In this review, we have briefly described the usefulness of certain biomarkers for the documentation of exposure and early biological effects, with special concern for the prediction of cancer. An emphasis is placed on understanding the biological and health significance of biomarkers. By building reliable biomarker databases, a promising future is the integration of information from the genome programs to expand the scientific frontiers on etiology, health risk prediction and prevention of environmental disease.     

Derivative component analysis for mass spectral serum proteomic profiles

Background

As a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage.

Methods

In this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers.

Results

Our results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis.

Conclusions

Our findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

     

Cadmium, osteoporosis and calcium metabolism

Occupational exposure to cadmium has for long been associated with renal tubular cell dysfunction, osteomalacia with osteoporosis, hypercalciuria and renal stone formation. High environmental exposure in Japan resulting from a stable diet of cadmium contaminated rice caused itai-itai disease, fractures occurring mainly in elderly multiparous women, with a form of osteomalacia, osteoporosis and renal dysfunction. More recently a population based study in Europe, in the vicinity of zinc smelters has shown that low to moderate exposure to cadmium, with a mean urinary excretion of cadmium of the order of 1 microg/g creatinine has been associated with a decrease in bone density, an increased risk of bone fractures in women and of height loss in men. In a population-based study of residents near a cadmium smelter in China, forearm bone density was shown to decrease linearly with age and urinary cadmium in both sexes, suggesting a dose effect relationship between cadmium dose and bone mineral density. A marked increase in the prevalence of fractures was shown in the cadmium-polluted area in both sexes. Concentrations of cadmium in blood and urine were taken as exposure biomarkers, and beta2-microglobulin, retinol binding protein and albumin as biomarkers of effect. A marked dose response relationship between these indicators of exposure and effect was shown. Hypercalciuria, which may progress to osteoporosis, has been taken as a sensitive renal-tubular biomarker of a low level of cadmium exposure. Cadmium may also act directly on bone. Animal studies have shown cadmium to stimulate the formation and activity of osteoclasts, breaking down the collagen matrix in bone. Osteoporosis is the main cause of fracures in post-menopausal women, a common occurrence worldwide, giving rise to disability and a high cost to health services. The identification of cadmium, an environmental pollutant, as one causal factor is highly significant in helping to control the incidence of this complex condition.