Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates

Mendelian randomization methods, which use genetic variants as instrumental variables for exposures of interest to overcome problems of confounding and reverse causality, are becoming widespread for assessing causal relationships in epidemiological studies. The main purpose of this paper is to demonstrate how results can be biased if researchers select genetic variants on the basis of their association with the exposure in their own dataset, as often happens in candidate gene analyses. This can lead to estimates that indicate apparent “causal” relationships, despite there being no true effect of the exposure. In addition, we discuss the potential bias in estimates of magnitudes of effect from Mendelian randomization analyses when the measured exposure is a poor proxy for the true underlying exposure. We illustrate these points with specific reference to tobacco research.     

不同制图尺度及土壤数据对土壤全磷储量估算的影响

准确计算农田土壤磷储量对农业可持续发展和面源污染治理具有重要意义,但以往的磷储量研究并没有考虑不同土壤数据源和制图尺度造成的估算误差.本文以江苏北部29个县(市)约393×10⁴ hm²旱地为例,分析了我国《县级土种志》、《地级市土种志》、《省级土种志》和《中国土种志》中记录土壤剖面资料分别建立的1∶5万、1∶25万、1∶50万、1∶100万、1∶400万和1∶1000万数据库对土壤全磷储量估算的影响.结果表明: 与数据最详细、记录有983个土壤剖面的《县级土种志》1∶5万尺度全磷密度和储量相比,其他不同土壤数据源建立的各个尺度土壤数据库估算的全磷密度和储量相对偏差分别在4.8%～48.9%和1.6%～48.4%.大部分《县级土种志》和《地级市土种志》土壤数据源建立的不同尺度全磷密度与《县级土种志》1∶5万尺度之间存在极显著或显著差异,《省级土种志》和《中国土种志》土壤数据源建立的不同尺度全磷密度与《县级土种志》1∶5万尺度之间均存在极显著差异,说明在旱地磷储量估算的研究中,选择适宜的制图尺度和土壤数据源是非常必要的.     

Ecdysteroids in spinach (Spinacia oleracea L.): biosynthesis, transport and regulation of levels

Many plant species produce phytoecdysteroids (PEs: i.e. analogues of insect steroid hormones). There is increasing evidence that PEs are used as a chemical defence by plants against non-adapted insects and nematodes. PEs are good candidates for the development of an environmentally safe approach to crop protection. Most crop species do not accumulate PEs. However, many arguments support the idea that most, if not all, plant species have the genetic ability to produce PEs, but the biosynthetic pathway is not active. A better understanding of the PE biosynthetic pathway and its regulation is consequently necessary. Spinach is one of the very few crop plants which produce large amounts of PEs, of which 20-hydroxyecdysone is the major component. Labeling experiments with radiolabeled precursor (mevalonic acid), putative ecdysteroid intermediates and 20-hydroxyecdysone itself have allowed investigation of PE biosynthesis and transport during spinach development. Biosynthesis takes place in older leaf sets ("sources"), but not in the young developing ones, which in contrast accumulate (acting as "sinks") the PEs produced by the older leaves. PEs are thus continuously redistributed within the developing plant, as its leaf set number increases. The biosynthetic pathway has been analyzed using excised leaves and various labeled precursors, and a preferential sequence of the last steps has been established. Although they do not produce PEs, apical leaf sets are nevertheless able to perform several putative terminal steps of PE biosynthesis. The regulatory mechanisms of PE synthesis appear to involve a direct negative feedback of 20-hydroxyecdysone (the major PE in spinach) on its own synthesis; thus, a sustained synthesis in older leaves requires that they can export the PE they produce.     

In-depth Proteomic Analysis of Six Types of Exudative Pleural Effusions for Nonsmall Cell Lung Cancer Biomarker Discovery

Pleural effusion (PE), a tumor-proximal body fluid, may be a promising source for biomarker discovery in human cancers. Because a variety of pathological conditions can lead to PE, characterization of the relative PE proteomic profiles from different types of PEs would accelerate discovery of potential PE biomarkers specifically used to diagnose pulmonary disorders. Using quantitative proteomic approaches, we identified 772 nonredundant proteins from six types of exudative PEs, including three malignant PEs (MPE, from lung, breast, and gastric cancers), one lung cancer paramalignant PE, and two benign diseases (tuberculosis and pneumonia). Spectral counting was utilized to semiquantify PE protein levels. Principal component analysis, hierarchical clustering, and Gene Ontology of cellular process analyses revealed differential levels and functional profiling of proteins in each type of PE. We identified 30 candidate proteins with twofold higher levels (q<0.05) in lung cancer MPEs than in the two benign PEs. Three potential markers, MET, DPP4, and PTPRF, were further verified by ELISA using 345 PE samples. The protein levels of these potential biomarkers were significantly higher in lung cancer MPE than in benign diseases or lung cancer paramalignant PE. The area under the receiver-operator characteristic curve for three combined biomarkers in discriminating lung cancer MPE from benign diseases was 0.903. We also observed that the PE protein levels were more clearly discriminated in effusions in which the cytological examination was positive and that they would be useful in rescuing the false negative of cytological examination in diagnosis of nonsmall cell lung cancer-MPE. Western blotting analysis further demonstrated that MET overexpression in lung cancer cells would contribute to the elevation of soluble MET in MPE. Our results collectively demonstrate the utility of label-free quantitative proteomic approaches in establishing differential PE proteomes and provide a new database of proteins that can be used to facilitate identification of pulmonary disorder-related biomarkers.The lungs are covered by parietal and visceral pleural membranes, including a small amount of fluid (10–20 ml) in the pleural cavity that helps the lungs expand and contract smoothly. Pleural effusions (PE)¹, an accumulation of pleural fluid, contain proteins originating from the plasma filtrate and are released by inflammatory or epithelial cells. PE is triggered by a variety of etiologies, including malignancies and benign diseases such as pneumonia (PN), tuberculosis (TB), pulmonary embolism, heart failure, renal dysfunction, and autoimmune disease (1). Based on their biochemical characteristics, PEs are classified as transudative or exudative; determination of the PE type is a crucial step in the differential diagnosis and management of PEs. Transudative effusions, generally caused by systemic diseases, can be effectively distinguished from exudative PEs using the established modified Light''s criteria (2, 3). However, further discrimination among different exudate types such as malignant and nonmalignant effusions (e.g. paramalignancies or acute and chronic inflammatory diseases) is sometimes diagnostically challenging because of similar biochemical and/or cellular profiles. For example, neutrophil-rich fluid is generally observed in patients with bacterial PN whereas lymphocytic effusions are generally observed in cancer or chronic inflammatory diseases such as TB (4).PEs caused by cancer are generally divided into two categories, malignant (MPE) and paramalignant (PMPE). MPEs result when cancer cells metastasize to the pleural cavity (stage IV), wherein exfoliated malignant cells are observed in pleural fluid by cytological examination or detected in percutaneous pleural biopsy, thoracoscopy, thoracotomy, or at autopsy (5). PMPE occurs in cancer patients with no evidence of tumor invasion in the pleural space and may be caused by airway obstruction with lung collapse, lymphatic obstruction, or the systemic effects of cancer treatment (5). A high percentage of MPEs (>75%) arise from lung, breast, and ovarian cancer or lymphoma/leukemia. Lung cancer is a major etiology underlying MPE (6); however, only ∼40–87% patients with MPE can be accurately diagnosed upon initial examination (7). Inaccurate diagnosis of MPE and PMPE underestimates or overestimates the disease stage and leads to inappropriate therapy. Thus, it is important to identify a specific and powerful biomarker to distinguish MPE from benign diseases and PMPE.Notably, tumor-proximal body fluids are promising sources for biomarker discovery because they represent a reservoir of in vivo tumor-secreted proteins without a large dynamic range or complexity of plasma or serum (8). Tumor-proximal fluids include PEs, nipple aspirate, stool, saliva, lavage, and ascites fluid. Previously, we utilized the powerful analytical capability of high-abundance protein depletion followed by one-dimensional SDS-PAGE combined with nano-LC-MS/MS (GeLC-MS/MS) for biomarker discovery to generate a comprehensive MPE proteome data set from 13 pooled nonsmall cell lung cancer (NSCLC) patients (9). Because a variety of pathological conditions can lead to exudative effusions, generating different PE proteomic profiles would accelerate discovery of potential PE biomarkers that can be used to discriminate between malignant and nonmalignant pulmonary disorders. The aim of this study is to establish differential PE proteomes from six types of exudative PEs, including three MPEs (from NSCLC, breast, and gastric cancers), one PMPE from NSCLC, and two benign diseases (TB and PN), using a label-free semiquantitative proteomics approach. Our results were verified by clinical validation of three potential biomarkers using an enzyme-linked immunosorbent assay (ELISA; Fig. 1).Open in a separate windowFig. 1.Biomarker discovery strategy for identifying differentially expressed proteins from six pleural effusion (PE) types. The strategy comprised prefractionation by removal of high-abundance proteins, GeLC-MS/MS, comparative analysis of the six PE proteomes based on spectral counts, proteome clustering, functional classification of differentially expressed proteins, and selection and validation of biomarker candidates by ELISA.     

LCA of Multicrystalline Silicon Photovoltaic Systems - Part 1: Present Situation and Future Perspectives (8 pp)

- Part 1: Present Situation and Future Perspectives Part 2: Application on an Island Economy Goal, Scope and Background Incorporation of exposure and risk concepts into life cycle impact assessment (LCIA) is often impaired by the number of sources and the complexity of site-specific impact assessment, especially when input-output (I-O) analysis is used to evaluate upstream processes. This makes it difficult to interpret LCIA outputs, especially in policy contexts. In this study, we develop an LCIA tool which takes into account the geographical variability in both emissions and exposure, and which can be applied to all economic sectors in I-O analysis. Our method relies on screening-level risk calculations and methods to estimate population exposure per unit of emissions from specific geographic locations. Methods We propose a simplified impact assessment approach using the concept of intake fraction, which is the fraction of a pollutant or its precursor emitted that is eventually inhaled or ingested by the population. Instead of running a complex site-specific exposure analysis, intake fractions allow for the accounting of the regional variability in exposure due to meteorological factors and population density without much computational burden. We calculate sector-specific intake fractions using previously-derived regression models and apply these values to the supply chain emissions to screen for the sectors whose emissions largely contribute to the total exposures. Thus, the analytical steps are simplified by relying on these screening-level risk calculations. We estimate population exposure per unit emissions from specific geographic locations only for the facilities and pollutants that pass an initial screening analysis. We test our analytical approach with reference to the case of increasing insulation for new single-family homes in the US. We quantify the public health costs from increasing insulation manufacturing and compare them with the benefits from energy savings, focusing on mortality and morbidity associated with exposure to primary and secondary fine particles (PM2.5) as well as cancer risk associated with exposure to toxic air pollutants. We estimate health impacts using concentration-response functions from the published literature and compare the costs and benefits of the program by assigning monetary values to the health risks. In the second part of this paper, we present the results of our case study and consider the implications for incorporating exposure and risk concepts into I-O LCA. Conclusions We have presented a methodology to incorporate regional variability in emissions and exposure into input-output LCA, using reduced-form information about the relationship between emissions and population exposure, along with standard input-output analysis and risk assessment methods. The location-weighted intake fractions can overcome the difficulty in incorporation of regional exposure in LCIA.     

A repeated measures approach to pooled and calibrated biomarker data

Participant-level meta-analysis across multiple studies increases the sample size for pooled analyses, thereby improving precision in effect estimates and enabling subgroup analyses. For analyses involving biomarker measurements as an exposure of interest, investigators must first calibrate the data to address measurement variability arising from usage of different laboratories and/or assays. In practice, the calibration process involves reassaying a random subset of biospecimens from each study at a central laboratory and fitting models that relate the study-specific “local” and central laboratory measurements. Previous work in this area treats the calibration process from the perspective of measurement error techniques and imputes the estimated central laboratory value among individuals with only a local laboratory measurement. In this work, we propose a repeated measures method to calibrate biomarker measurements pooled from multiple studies with study-specific calibration subsets. We account for correlation between measurements made on the same person and between measurements made at the same laboratory. We demonstrate that the repeated measures approach provides valid inference, and compare it to existing calibration approaches grounded in measurement error techniques in an example describing the association between circulating vitamin D and stroke.     

基于综合评价法的洞庭湖区绿地生态网络构建

绿地生态网络对于改善区域生态空间破碎化、生物多样性降低、生态系统服务供需不平衡及保障区域生态安全具有重要意义。本研究以洞庭湖区为例,在3S技术支持下,从生态系统服务功能、潜在生物多样性、形态空间格局的角度综合评价和识别生态源地及计算栅格单元的基本生态阻力;利用夜间灯光指数修正基本生态阻力;运用最小累积阻力模型识别生态廊道;构建加权评价模型,对源地的聚合度、离散度及廊道的生态连接贡献度进行评价;利用结构特征指数对综合网络、“源-汇”潜在网络及规划网络进行对比分析和评价。结果表明: 源地空间分布不均衡,林地、灌丛与水域三者面积之和占源地总面积的95.9%,位于研究区中部的洞庭湖湿地生态风险较高;源地离生态网络系统中心位置越近及到其他源地的平均最小累积阻力越小,聚合及离散的优势越强;高生态质量源地周围的中、高生态质量源地分布越密集,其聚合度、离散度越高;廊道离高生态质量的源地越近,表现为生态连接贡献度越大;林地、灌丛,尤其是河道在自然生态系统与人类社会系统之间起着非常重要的生态连接作用;“源-汇”规划绿道对“源-汇”潜在生态廊道形成了良好补充,与“源-汇”潜在网络相比,综合网络的α、β、γ、ρ指数分别提高123.1%、25.8%、26.2%、74.6%;与“源-汇”规划网络相比,α、β、γ、ρ指数分别提高了190.0%、31.1%、32.5%、114.6%。本研究结果能为洞庭湖区绿地生态网络构建、国土空间规划提供参考。     

Incorporation and remodeling of phosphatidylethanolamine containing short acyl residues in yeast

Phosphatidylethanolamine (PE) is one of the essential phospholipids in the yeast Saccharomyces cerevisiae. We have previously shown that a yeast strain, the endogenous PE synthesis of which was controllable, grew in the presence of PE containing decanoyl residues (diC10PE) when PE synthesis was repressed. In this study, we investigated the fate of diC10PE, its uptake and remodeling in yeast. Deletion of the genes encoding Lem3p/Ros3p or P-type ATPases, Dnf1p and Dnf2p, impaired the growth of the mutants in the medium containing diC10PE, suggesting the involvement of these proteins in the uptake of diC10PE. Analysis of the metabolism of deuterium-labeled diC10PE by electrospray ionization tandem mass spectrometry revealed that it was rapidly converted to deuterium-labeled PEs containing C16 or C18 acyl residues. The probable intermediate PEs that contained decanoic acid and C16 or C18 fatty acids as acyl residues were also detected. In addition, a substantial amount of decanoic acid was released into the culture medium during growth in the presence of diC10PE. These results imply that diC10PE was remodeled to PEs with longer acyl residues and used as membrane components. Defects in the remodeling of diC10PE in the deletion mutants of ALE1 and SLC1, products of which were capable of acyl-transfer to the sn− 2 position of lyso-phospholipids, suggested their involvement in the introduction of acyl residues to the sn− 2 position of lyso-phosphatidylethanolamine in the remodeling reaction of diC10PE. Our results also suggest the presence of a mechanism to maintain the physiological length of PE acyl residues in yeast.     

A Risk-Based Approach to Health Impact Assessment for Input-Output Analysis, Part 1: Methodology (7 pp)

Goal, Scope and Background Incorporation of exposure and risk concepts into life cycle impact assessment (LCIA) is often impaired by the number of sources and the complexity of site-specific impact assessment, especially when input-output (I-O) analysis is used to evaluate upstream processes. This makes it difficult to interpret LCIA outputs, especially in policy contexts. In this study, we develop an LCIA tool which takes into account the geographical variability in both emissions and exposure and which can be applied to all economic sectors in I-O analysis, relying on screening-level risk calculations and methods to estimate population exposure per unit emissions from specific geographic locations. Methods We develop our analytical approach with reference to the case of increasing insulation for new single-family homes in the US. We quantify the public health costs from increasing insulation manufacturing and compare them with the benefits from energy savings, focusing on mortality and morbidity associated with exposure to primary and secondary fine particles (PM2.5) as well as cancer risk associated with exposure to toxic air pollutants. We use OpenLC to estimate the incremental economic outputs induced by increased insulation and reduced fuel consumption and calculate emissions from a sector-specific pollution intensity matrix. We calculate sector-specific intake fractions (dimensionless ratios between the amount of pollutant intake and the amount of a pollutant emitted) using previously-derived regression models and apply these values to the supply chain emissions of fiberglass and fuel sources. We refine the exposure estimates for selected emission sites and pollutants that contribute significantly to total health impacts, running site-specific air dispersion models. We estimate health impacts using concentration-response functions from the published literature and compare the costs and benefits of the program by assigning monetary values to the health risks. In the second part of this paper, we present the results of our case study and consider the implications for incorporating exposure and risk concepts into I-O LCA.     

Cadmium Exposure in the South Korean Population: Implications of Input Assumptions for Deterministic Dietary Assessment

Dietary exposure to Cadmium (Cd) is of increasing interest globally because of the adverse health effects of Cd arising from multiple sources. The assumptions used when undertaking deterministic assessment of Cd in global or regional diets have implications when applied to specific national cases representing local variation in food composition and consumption patterns different from global or regional norms. We have conducted deterministic dietary Cd exposure assessments for the South Korean population using a variety of schemes for point estimation. Consumption data from the Korean Nutrition Survey (2001 to 2003) and monitoring data from the Korea Food and Drug Administration were used as the basis for the exposure estimates. The average daily per capita Cd exposure was 14 μ g for the South Korean population, representing about 27% of tolerable daily intake (TDI) and is similar to that reported in other countries. The hazard index (HI, the ratio of total Cd exposure to the TDI) typically ranged from 0.3 to 0.9 depending on assumptions used in deterministic estimates of dietary exposure. Even though the current exposure of the South Korean population at large is found to be safe on the basis of these estimates, consideration of high-end patterns of Cd level and consumption suggests the need for continued vigilance in dietary Cd monitoring.     

Effect of prey mass and selection on predator carrying capacity estimates

The ability to determine the prey-specific biomass intake of large predators is fundamental to their conservation. In the absence of actual prey data, researchers generally use a “unit mass” method (estimated as 3/4 adult female mass) to calculate the biomass intake of predators. However, differences in prey preference and range across geographic regions are likely to have an influence on biomass calculations. Here we investigate the influence of estimated prey mass on leopard biomass calculations, and subsequent carrying capacity estimates, in an understudied mountain population. Potential leopard feeding sites were identified using global positioning system (GPS) location clusters obtained from GPS collars. We investigated 200 potential leopard feeding sites, of which 96 were actual feeding sites. Jaw bones, horns, hooves, and other indicative bones were used to determine gender and age of prey items, which were subsequently used to calculate mass of each prey item based on previously published values. There were significant differences in the biomass values calculated using the traditional unit mass method and the calculated prey masses obtained from leopard feeding sites. However, there were no considerable differences in the carrying capacity estimates using the preferred prey species model and leopard density estimates calculated using a non-biased spatial approach, which suggests that estimating carnivore carrying capacity based on 3/4 adult female masses is a reliable method also for the mountain population in this study.     

The “deduction” approach: A non-invasive method for estimating secondary production of earthworm communities

Secondary production is an important parameter for the study of population dynamics and energy flow through animal communities. Secondary production of earthworm communities has been determined with the size-frequency and instantaneous growth rate methods, whereby earthworm populations are repeatedly sampled at regular intervals and the change in biomass of cohorts or individuals between sampling dates is determined. The major disadvantage of repeated sampling is that it disturbs the soil and permanently removes earthworms from the study area. The “deduction” approach is a theoretical model that partitions individuals into defined pools and makes assumptions about the growth, recruitment and mortality of each pool. In 2004 and 2005, earthworms were added to undisturbed field enclosures and the “deduction” approach was used to estimate secondary production of the indigenous and added earthworm populations during the crop growing period (17–18 weeks) in each year. Secondary production estimates made by the “deduction” approach were similar to estimates from direct earthworm sampling in temperate agroecosystems. The “deduction” approach is an indirect method that estimates population dynamics and secondary production, and is appropriate for manipulation experiments where removal of organisms and physical disturbance of the habitat by repeated sampling could bias results.     

Quantitative methods for defining mast‐seeding years across species and studies

Abstract: Although there is a quantitative method that is commonly used for identifying mast‐seeding behaviour of a plant population based on the coefficient of variation (i.e. CV is standard deviation/mean>1), there is no general quantitative method for delineating “mast” as opposed to “non‐mast” years. Mast years are, however, described qualitatively as years when “large”, “unusually large” and “high” seed production occurs. The use of a consistent and generally applicable method for delineating mast years across species and plant populations is important for synthesizing knowledge of the causes and consequences of mast seeding, which could be confounded by using different methods among studies. We examine six quantitative methods for identifying mast years: four methods from the literature and two methods developed here. We use 36 seed production datasets covering a variety of species with ≥10 years of data to test the performance of these six methods. For each method, we quantify the percentage of the datasets to which the method could be successfully applied, the magnitude of the mast year relative to the mean, the frequency of mast years and the occurrence of consecutive mast years. The majority of the methods failed to meet the criteria for a suitable method. The best method used the number of standard deviates (standardized deviate method) of the annual mean seed production from the long‐term mean of the dataset to identify mast‐seeding years. General results from the standardized deviate method include that the occurrence of mast‐seeding years is largely unrelated to plant population CV, but similar across species and data collection methods.     

Reducing overestimation in reported mobile phone use associated with epidemiological studies

Case-control studies of mobile phones are commonly based on retrospective, self-reported exposure information, which are often characterized as involving substantial uncertainty concerning data validity. We assessed the validity of self-reported mobile phone use and developed a statistical model to account for the over-reporting of exposure. We collected information on mobile phone use from 70 volunteers using two sources of data: self-report in an interview and network operator records. We used regression models to obtain bias-corrected estimates of exposure. A correlation coefficient of 0.71 was obtained between the self-reported and the network operators' data on average calling time (log-transformed minutes per month). A simple linear regression model, where the duration of calls acquired from network operators is explained with the self-reported duration fitted the data reasonably well (adjusted R(2) 0.51). The constant term was 2.71 and the regression coefficient 0.49 (logarithmic scale). No significant improvement in the model fit was achieved by including potential predictors of accuracy in self-reported exposure estimates, such as the pattern of mobile phone use, the modality of response to the questionnaire or demographic characteristics. Overestimation in self-reported intensity of mobile phone use can be accounted for by the use of regression calibration. The estimates obtained in our study may not be applicable in other contexts, but similar methods could be used to reduce bias in other studies.     

Upscaling Regional Emissions of Greenhouse Gases from Rice Cultivation: Methods and Sources of Uncertainty

One of the important sources of greenhouse gases is the emission of methane from rice fields. Methane emission from rice fields is the result of a complex array of soil processes involving plant-microbe interactions. The cumulative effects of these processes at the level of individual plants influence the global atmospheric composition and make it necessary to expand our research focus from small plots to large landscapes and regions. However, present extrapolations (‘upscaling’) are tenuous at best because of methodological and practical problems. The different steps taken to calculate regional emission strengths are discussed and illustrated by calculations for a case-study in the Philippines. The applicability of high quality, process-based, models of methane emission at the level of individual plants is limited for regional analysis by their large data requirements. Simplified models can be used at the regional level but are not able to capture the complex emission situation. Data availability and model accuracy are therefore often difficult to match. Other common sources of uncertainty are the quality of input data. A critical evaluation of input data should be made in every upscaling study to assess the suitability for calculating regional emissions. For the case-study we show effects of differences in input data caused by data source and interpolation technique. The results from the case-study and similar studies in literature indicate that upscaling techniques are still troublesome and a cause of large uncertainties in regional estimates. The results suggest that some of the stumbling blocks in the conventional upscaling procedure are almost impassable in the near future. Based on these results, a plea is made for meso-level measurements to calibrate and validate upscaling methods in order to be better able to quantify and reduce uncertainties in regional emission estimates.     

Exposure reconstruction for reducing uncertainty in risk assessment: example using MTBE biomarkers and a simple pharmacokinetic model

Adverse health risks from environmental agents are generally related to average (long-term) exposures. Because a given individual's contact with a pollutant is highly variable and dependent on activity patterns, local sources and exposure pathways, simple ‘snapshot’ measurements of surrounding environmental media may not accurately assign the exposure level. Furthermore, susceptibility to adverse effects from contaminants is considered highly variable in the population so that even similar environmental exposure levels may result in differential health outcomes in different individuals. The use of biomarker measurements coupled to knowledge of rates of uptake, metabolism and elimination has been suggested as a remedy for reducing this type of uncertainty. To demonstrate the utility of such an approach, we invoke results from a series of controlled human exposure tests and classical first-order rate kinetic calculations to estimate how well spot measurements of methyl tertiary butyl ether and the primary metabolite, tertiary butyl alcohol, can be expected to predict different hypothetical scenarios of previous exposures. We found that blood and breath biomarker measurements give similar results and that the biological damping effect of the metabolite production gives more stable estimates of previous exposure. We also explore the value of a potential urinary biomarker, 2-hydroxyisobutyrate suggested in the literature. We find that individual biomarker measurements are a valuable tool in reconstruction of previous exposures and that a simple pharmacokinetic model can identify the time frames over which an exogenous chemical and the related chemical biomarker are useful. These techniques could be applied to broader ranges of environmental contaminants to assess cumulative exposure risks if ADME (Absorption, Distribution, Metabolization and Excretion) is understood and systemic biomarkers can be measured.     

Evaluation of methods used to assess dietary intake: simulation analyses

The choice of dietary methodology can affect the ability to detect and describe the relationship between dietary sodium intake and blood pressure. This is illustrated in this paper through the use of simulation modelling of the effect of using different dietary methods (food recalls or records covering different numbers of days, food frequency questionnaire estimates of a single diet component) and using urinary excretion as a proxy for intake. Both epidemiologic studies and experimental interventions are simulated. Although the data base used was simulated rather than real, an attempt was made to keep it realistic in relation to what might be seen in actual populations. From these analyses it can be inferred that with appropriate choice of methodology and study design, even low order relationships between sodium intake and blood pressure should be detectable. At a more general level, it may be concluded that while there is no perfect dietary methodology, there are preferred methodologies for defined purposes.     

Statistical Aspects of the Use of Biomarkers in Nutritional Epidemiology Research

Few strong and consistent associations have arisen from observational studies of dietary consumption in relation to chronic disease risk. Measurement error in self-reported dietary assessment may be obscuring many such associations. Attempts to correct for measurement error have mostly used a second self-reported assessment in a subset of a study cohort to calibrate the self-reported assessment used throughout the cohort, under the dubious assumption of uncorrelated measurement errors between the two assessments. The use, instead, of objective biomarkers of nutrient consumption to produce calibrated consumption estimates provides a promising approach to enhance study reliability. As summarized here, we have recently applied this nutrient biomarker approach to examine energy, protein, and percent of energy from protein, in relation to disease incidence in Women’s Health Initiative cohorts, and find strong associations that are not evident without biomarker calibration. A major bottleneck for the broader use of a biomarker-calibration approach is the rather few nutrients for which a suitable biomarker has been developed. Some methodologic approaches to the development of additional pertinent biomarkers, including the possible use of a respiratory quotient from indirect calorimetry for macronutrient biomarker development, and the potential of human feeding studies for the evaluation of a range of urine- and blood-based potential biomarkers, will briefly be described.     

Exposure reconstruction for reducing uncertainty in risk assessment: example using MTBE biomarkers and a simple pharmacokinetic model

Adverse health risks from environmental agents are generally related to average (long-term) exposures. Because a given individual's contact with a pollutant is highly variable and dependent on activity patterns, local sources and exposure pathways, simple 'snapshot' measurements of surrounding environmental media may not accurately assign the exposure level. Furthermore, susceptibility to adverse effects from contaminants is considered highly variable in the population so that even similar environmental exposure levels may result in differential health outcomes in different individuals. The use of biomarker measurements coupled to knowledge of rates of uptake, metabolism and elimination has been suggested as a remedy for reducing this type of uncertainty. To demonstrate the utility of such an approach, we invoke results from a series of controlled human exposure tests and classical first-order rate kinetic calculations to estimate how well spot measurements of methyl tertiary butyl ether and the primary metabolite, tertiary butyl alcohol, can be expected to predict different hypothetical scenarios of previous exposures. We found that blood and breath biomarker measurements give similar results and that the biological damping effect of the metabolite production gives more stable estimates of previous exposure. We also explore the value of a potential urinary biomarker, 2-hydroxyisobutyrate suggested in the literature. We find that individual biomarker measurements are a valuable tool in reconstruction of previous exposures and that a simple pharmacokinetic model can identify the time frames over which an exogenous chemical and the related chemical biomarker are useful. These techniques could be applied to broader ranges of environmental contaminants to assess cumulative exposure risks if ADME (Absorption, Distribution, Metabolization and Excretion) is understood and systemic biomarkers can be measured.